{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:21:52.321595Z",
     "start_time": "2021-03-25T03:21:50.834354Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision \n",
    "from torchvision import transforms\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "from tqdm.autonotebook import tqdm\n",
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import time\n",
    "\n",
    "from idlmam import train_network, Flatten, weight_reset, set_seed\n",
    "from idlmam import LanguageNameDataset, pad_and_pack, EmbeddingPackable, LastTimeStep, LambdaLayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:21:52.326826Z",
     "start_time": "2021-03-25T03:21:52.323249Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('png', 'pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:21:52.341397Z",
     "start_time": "2021-03-25T03:21:52.327961Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "torch.backends.cudnn.deterministic=True\n",
    "set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:21:52.395597Z",
     "start_time": "2021-03-25T03:21:52.343142Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:21:52.526680Z",
     "start_time": "2021-03-25T03:21:52.405437Z"
    }
   },
   "outputs": [],
   "source": [
    "train_data = torchvision.datasets.FashionMNIST(\"./\", train=True, transform=transforms.ToTensor(), download=True)\n",
    "test_data = torchvision.datasets.FashionMNIST(\"./\", train=True, transform=transforms.ToTensor(), download=True)\n",
    "\n",
    "train_loader = DataLoader(train_data, batch_size=128, shuffle=True)\n",
    "test_loader = DataLoader(test_data, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:21:52.531487Z",
     "start_time": "2021-03-25T03:21:52.528730Z"
    }
   },
   "outputs": [],
   "source": [
    "#Whats the width and height of our images?\n",
    "W, H = 28, 28 #\n",
    "#How many values are in the input? We use this to help determine the size of subsequent layers\n",
    "D = 28*28 #28 * 28 images \n",
    "#Hidden layer size\n",
    "n = 256 \n",
    "#How many channels are in the input?\n",
    "C = 1\n",
    "#how many filters per convolutional layer\n",
    "n_filters = 32\n",
    "#How many classes are there?\n",
    "classes = 10#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:21:52.542462Z",
     "start_time": "2021-03-25T03:21:52.532925Z"
    }
   },
   "outputs": [],
   "source": [
    "fc_model = nn.Sequential(\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D,  n), nn.Tanh(), #First hidden layer \n",
    "    *[nn.Sequential(nn.Linear(n,  n),nn.Tanh()) for _ in range(5)], #Now that each remaining layer has the same input/output sizes, we can make them with a list unpacking\n",
    "    nn.Linear(n, classes),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:21:52.553681Z",
     "start_time": "2021-03-25T03:21:52.544009Z"
    }
   },
   "outputs": [],
   "source": [
    "cnn_model = nn.Sequential(\n",
    "    nn.Conv2d(C, n_filters, 3, padding=1),             nn.Tanh(),\n",
    "    nn.Conv2d(n_filters, n_filters, 3, padding=1),     nn.Tanh(),\n",
    "    nn.Conv2d(n_filters, n_filters, 3, padding=1),     nn.Tanh(),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    nn.Conv2d(  n_filters, 2*n_filters, 3, padding=1), nn.Tanh(),\n",
    "    nn.Conv2d(2*n_filters, 2*n_filters, 3, padding=1), nn.Tanh(),\n",
    "    nn.Conv2d(2*n_filters, 2*n_filters, 3, padding=1), nn.Tanh(),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    nn.Conv2d(2*n_filters, 4*n_filters, 3, padding=1), nn.Tanh(),\n",
    "    nn.Conv2d(4*n_filters, 4*n_filters, 3, padding=1), nn.Tanh(),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D*n_filters//4, classes),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:39.986824Z",
     "start_time": "2021-03-25T03:21:52.555081Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d4ee4c0fe9b4ef49a2cae5c43efe5eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad2186812f3d4d49a27931a994faf3c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "loss_func = nn.CrossEntropyLoss()\n",
    "fc_results = train_network(fc_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "cnn_results = train_network(cnn_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:39.990598Z",
     "start_time": "2021-03-25T03:26:39.987890Z"
    }
   },
   "outputs": [],
   "source": [
    "del fc_model\n",
    "del cnn_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:40.228925Z",
     "start_time": "2021-03-25T03:26:39.991854Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_results, label='Fully Connected')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_results, label='CNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:40.726297Z",
     "start_time": "2021-03-25T03:26:40.230183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sigmoid(x):\n",
    "    return np.exp(activation_input)/(np.exp(activation_input)+1)\n",
    "\n",
    "activation_input = np.linspace(-5, 5, num=200)\n",
    "tanh_activation = np.tanh(activation_input)\n",
    "sigmoid_activation = sigmoid(activation_input)\n",
    "\n",
    "sns.lineplot(x=activation_input, y=tanh_activation, color='red', label=\"tanh(x)\")\n",
    "sns.lineplot(x=activation_input, y=sigmoid_activation, color='blue', label=\"$\\sigma(x)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:40.976559Z",
     "start_time": "2021-03-25T03:26:40.727608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def tanh_deriv(x):\n",
    "    return 1.0 - np.tanh(x)**2\n",
    "def sigmoid_derivative(x):\n",
    "    return sigmoid(x)*(1-sigmoid(x))\n",
    "\n",
    "tanh_deriv = tanh_deriv(activation_input)\n",
    "sigmoid_deriv = sigmoid_derivative(activation_input)\n",
    "\n",
    "sns.lineplot(x=activation_input, y=tanh_deriv, color='red', label=\"tanh'(x)\")\n",
    "sns.lineplot(x=activation_input, y=sigmoid_deriv, color='blue', label=\"$\\sigma'(x)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:41.293240Z",
     "start_time": "2021-03-25T03:26:40.977797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "activation_input = np.linspace(-5, 5, num=200)\n",
    "relu_activation = np.maximum(0,activation_input)\n",
    "leaky_relu_activation = np.maximum(0.3*activation_input,activation_input)\n",
    "\n",
    "sns.lineplot(x=activation_input, y=tanh_activation, color='red', label=\"tanh(x)\")\n",
    "sns.lineplot(x=activation_input, y=sigmoid_activation, color='blue', label=\"$\\sigma(x)$\")\n",
    "sns.lineplot(x=activation_input, y=relu_activation, color='green', label=\"ReLU(x)\")\n",
    "sns.lineplot(x=activation_input, y=leaky_relu_activation, color='purple', label=\"LeakyReLU(x)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:41.609246Z",
     "start_time": "2021-03-25T03:26:41.294602Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAA7CklEQVR4nO2deXhUVfK/35OEfTcBZFOiIgpIAokgIAgiCqigIG4oo4i4ovMb8es6DjqOIgNuIzMIyqAOCiKKqCwugGIihEDCLggIGgi7QFgCSfr8/qh0SEKWTtLdt7tT7/Pcp7vvPX1vdXL603Xr1KljrLUoiqIowU+Y0wYoiqIo3kEFXVEUJURQQVcURQkRVNAVRVFCBBV0RVGUECHCqQtHRUXZli1bOnV5RVGUoGTlypX7rbUNizrmmKC3bNmS5ORkpy6vKIoSlBhjdhR3TEMuiqIoIYIKuqIoSoiggq4oihIiOBZDL4qsrCzS0tLIzMx02hSlGKpXr07z5s2pUqWK06YoilKIgBL0tLQ06tSpQ8uWLTHGOG2OUghrLQcOHCAtLY3o6GinzVEUpRClhlyMMVONMXuNMeuKOW6MMW8aY7YYY9YYYzqW15jMzEwiIyNVzAMUYwyRkZF6B6UoAYonMfRpQN8SjvcDWuVuI4H/VMQgFfPARv8/ihK4lBpysdb+YIxpWUKTgcD7VurwLjPG1DfGNLHWpnvLSEXxG0uWkJaSytTFW8ipdxboD5jiA9pe2Zab77rZ6+f1Rgy9GfB7vtdpufvOEHRjzEjEi+ecc87xwqUVxUscPAgPPAAff8xnLTpgfx9IGGDR9QIU77M2a23ACnpRLkyR3wJr7WRgMkB8fHzAfVMOHTrEhx9+yIMPPliu9/fs2ZPx48cTHx9fYP+SJUuYNm0a06ZNA2DOnDmsWbOG5557rthzjR49mv79+3PllVeWyxalDGRnw003wY8/wosvkr3Xwps53N5sMq3CI2DVKoiMdNpKRSkVb+ShpwEt8r1uDuzywnn9zqFDh/j3v//t8+uMGzeu1B+NUaNGMXbsWJ/bogDPPguLF8OUKfDMM9jqNQEIm/gf2L0bhg4Fl8thIxWldLzhoc8FHjbGzAA6A4e9Ej//858hNbXCpylAbCy8/nqxh5988km2bt1KbGwsvXr1Ys2aNfzxxx9kZWXx4osvMnDgQLZv306/fv24/PLLSUxMpFmzZnz++efUqFEDgFmzZvHggw9y6NAh3n33Xbp3707VqlWpV68eAJs3b6ZatWpERUUBMHDgQAYPHsywYcN4++23+eGHH5g+fTrnnnsuBw4cYPfu3Zx99tne/Tsop1m7FsaNgxEj4E9/AsDmyM1jeEx7eOMNCcV8+CHccYeTlipKqZQq6MaYj4CeQJQxJg34G1AFwFo7CZgH9Ae2AMeBu31lrK8ZO3Ys69atIzU1lezsbI4fP07dunXZv38/l112GQMGDADgl19+4aOPPmLKlCncfPPNzJ49mztyv+zZ2dkkJSUxb948nn/+eb799lu6du1K165dAUhISKBjx9OZnZMnT6Zbt25ER0czYcIEli1blnesY8eOJCQkMHjwYD/+FSoZzz4LdevCK6/k7XLliDceHhEOI0fC22/Dc8/BzTdD1apOWaoopeJJlsttpRy3wENes8hNCZ60P7DW8vTTT/PDDz8QFhbGzp072bNnDwDR0dHExsYCEBcXx/bt2/PeN2jQoCL3u0lPT6dhw9OVLxs3bswLL7xAr169+OyzzzjrrLPyjjVq1Ihdu4IyehUcLFsGc+fCiy9Cvr+720OPiIiAsDB46SXo3x/eeQfKOb6iKP5Aa7kUw/Tp09m3bx8rV64kNTWVxo0b502oqVatWl678PBwsrOz8167jxXe76ZGjRpnTMxZu3YtkZGRZ4h3ZmZmXihH8QHjx4uQP/pogd3WlRtyiQiXHX37QteuMGGCxtKVgEYFPR916tQhIyMDgMOHD9OoUSOqVKnC4sWL2bGj2BLEZeLiiy9my5Ytea+TkpKYP38+KSkpjB8/nl9//TXv2ObNm2nXrp1XrqsUIj0d5syB4cOhdu0Ch/JCLuG5gm4MPPwwbNsG337rZ0MVxXNU0PMRGRlJt27daNeuHampqSQnJxMfH8/06dO56KKLvHKNHj16kJKSgrWWkydPcu+99zJ16lSaNm3KhAkTGD58ONZasrKy2LJlyxkpkIqXmDoVcnIkRl6IMzx0gEGDICpK4umKEqAEVHGuQODDDz8stc26dafL2owePTrv+ZIlS/KeR0VFFRlDr1mzJldddRXfffcdV111FatXr847NmDAgLyB1y+//JKbbrpJ4riKd3G5YPJk6N0bWrU643Belkt+Qa9WDe6+G159FXbtgqZN/WWtoniMeugO8PTTT3P8+PES22RnZ/PYY4/5yaJKRkIC/PabhFuKIE/Qw8MLHrjnHvHqZ83ytYWKUi5U0B2gcePGeZ54cQwZMoT69ev7x6DKxqxZ4nFff32Rh22OxWVchIcVEvTWraF9exV0JWBRQVcqFy4XfPKJpCHWqVNkE+uyWGMJM0V8PYYMEQ8/Lc3HhipK2VFBVyoXCQmS4TJkSLFNShV0gNmzfWSgopQfFXSlcjF7toRbrruu2CY2x+IKcxUt6O6wiwq6EoCooCuVi3nz4Moriw23gAi6Nbb4xTwGDIDERPjjDx8ZqSjlQwW9DHz22WdOm6BUhF9+ka1//xKbWZd46MXSr59ku3zzjZcNVJSKoYLuIVu3buXbfLMET5w4wRVXXEFOTk6R7U+dOkWPHj2KnP6vOMT8+fLYr1/J7XLAhpVQrr9zZykZMG+e92xTFC+ggu4hCxcuZNSoUXmvp06dyqBBg87MVc6latWq9O7dm5kzZ/rLRKU05s+HCy+E888vsZk75FIs4eFw9dWwYIHWdlECioCdhuhAOfQ8Vq9ezahRo9i/fz8///wz1lrOPvvsAotSTJ8+PW9Waa9evXj66afp06cPzz77LEeOHOHNN9/khhtu4KmnnmLo0KHe/SBK2Tl+XBaxeOCBUpu6s1xKpH9/mDEDUlIgLs5LRipKxQhYQXeKzMxMbrnlFt5//306derEX//6VzIzMxk3blxem1OnTrFt2zZatmwJwPPPP89zzz3H3r17SUlJYe7cuQC0a9eOFStWOPExlMIkJMDJk+JZl4J12ZJDLnD6PN9+q4KuBAwBK+hOlUP/9ttv6dixI506dQKgffv2LFiwoEDGw/79+wvM4uzRowfWWl599VWWLFmSF4YJDw+natWqZGRkUKeErArFDyxaBBER0L176W1Li6EDNG4MbdvKeZ94wjs2KkoF0Rh6IdatW8cll1yS93rVqlUFVhiCM2uar127lvT0dKpVq3aGcJ88eZLq1av71mildBYtgk6dziiVWxTWJVP/S+XKK2HpUjh1ygsGKkrFUUEvRGRkJGvWrAGkHvmnn37KrbfeWqBNgwYNyMnJITMzk/T0dIYOHcrnn39OrVq1WLhwYV67AwcO0LBhQ6pUqeLXz6AU4vBhSE4WAfYETzx0kPOdOAHLl1fMPkXxEirohbjttts4evQo7dq1Y+TIkXz00UdERkae0e7qq6/m66+/ZtCgQUyYMIGLL76Yv/71r4wZMyavzeLFi+lfSs6z4gd++EGyUTwUdGs9iKEDXHGFLH6xaFEFDVQU7xCwMXSnqF27Nl988UWp7R5++GFeffVVfvrpp7x9PXr0KPD6ww8/5OWXX/aJnUoZWLxYpvt36eJZ+xygmEmiBWjQADp2FEH/298qYqGieAX10MtJhw4d6NWrV4kTi2644QZat27tZ8uUM1i6VCYDeTqW4fIw5ALQowckJWkcXQkIVNArwPDhw0ucWDRs2DA/W6ScwbFjkiverZvHb7E5HoZcQM6bmSnXUBSHUUFXQpsVK6TuSteunr+nLB66+7wJCWW3TVG8jAq6EtokJsqjp/FzEEEvbaaomyZNIDr69HUUxUFU0JXQJiEBLroIishUKhYXZftmdO0q17Ee/ggoio9QQVdCF5cLfvqpTPFz8HDqf366dYPdu2H79rLZpyheRgVdCV1+/lkWoShL/BzK56GDxtEVx1FBV0IXd1y7jB46OWWIoQO0aycrIGkcXXEYFfRChIeHExsbS7t27bj++us5dOhQie3HjBnD+PHjC+y76667+OSTTwrsq51bQ2T79u307Nkzb39KSgojRowo8RpvvfUW//3vfz3/EIqQkCCx8wsvLNv7yuqhh4fDZZeph644jgp6IWrUqEFqairr1q3jrLPOYuLEiT693ksvvVRg4YyiGD58OG+++aZP7QhJEhMlHFLc2qDFUZa0RTfdusHatXDkSNnepyheJGCn/v95wZ9J3Z3q1XPGnh3L631f97h9ly5d8gp1bd26lYceeoh9+/ZRs2ZNpkyZwkUXXVRmG8LDwznrrLMAyMjIYM2aNcTExADwyCOPEBUVxXPPPcfChQv5xz/+wZIlS6hZsyYtW7YkKSkpr6yvUgr79sHmzTB8eNnf6wLKWk+tWzfJclm2zKOa64riCzzy0I0xfY0xm4wxW4wxTxZxvJ4x5gtjzGpjzHpjzN3eN9W/5OTk8N133zFgwAAARo4cyb/+9S9WrlzJ+PHjC6xeVBZatGjBp59+CkBycjLt2rXLOzZ27FhmzpzJ4sWLeeSRR/jvf/9LWJj8i+Lj41m6dGkFP1Ulwl1Tp6wDolD2kAtIaYGwMI2jK45SqodujAkHJgJ9gDRghTFmrrV2Q75mDwEbrLXXG2MaApuMMdOtteUucFEWT9qbnDhxgtjYWLZv305cXBx9+vTh6NGjJCYmMmTIkLx2J0+eLPYcpohb/KL2paen07Bhw7zXbs+/R48evPbaa5yfb+3LRo0a8fPPP5f3Y1U+EhKgShWIjy/zW43LlD3kUqcOtG+vcXTFUTzxQzoBW6y123IFegYwsFAbC9Qxolq1gYNAUC53746h79ixg1OnTjFx4kRcLhf169cnNTU1b9u4cWOx54iMjOSPP/7Ie33w4EGioqKKvFb+hTJAFsuIjIxk165dBfZnZmZSo0aNCn66SsSyZdChA5Tnb1YeDx1kNury5bpwtOIYnnTbZsDv+V6n5e7Lz1vAxcAuYC3wqLX2jF5tjBlpjEk2xiTv27evnCb7h3r16vHmm28yfvx4atSoQXR0NLNmzQKkXvbq1auLfW/Pnj2ZOXMmp3Ir8E2bNo1evXqd0e7iiy9my5Ytea937NjBhAkTSElJYf78+SzPt3DC5s2bC4RnlBLIyYFVq+DSS8v3/vIKeqdOkJEBmzaV77qKUkE86bZFpQgUvh+9BkgFmgKxwFvGmLpnvMnaydbaeGttfP5QQ6DSoUMHYmJimDFjBtOnT+fdd98lJiaGtm3b8vnnn+e1e/HFF2nevHnedt1119G9e3fi4uKIjY0lISGBV1555YzzX3TRRRw+fJiMjAystdxzzz2MHz+epk2b8u677zJixIg8Dz4hIYGrrrrKb589qNm8GY4eLVe4BShflguc/gHRhcEVp7DWlrgBXYCF+V4/BTxVqM1XQPd8rxcBnUo6b1xcnC3Mhg0bztgX6rz66qt2ypQpJbZZtWqVveOOO/xkUekE/P/p/fetBWvXri3X259o/IQd2Wlk2d+YnW1trVrWPvxwua6rKJ4AJNtidNUTD30F0MoYE22MqQrcCswt1OY3oDeAMaYx0BrYVsHfmkrBAw88QLVq1Upss3//fv7+97/7yaIQIDkZataUolzloFyDoiATjOLj1UNXHKPULBdrbbYx5mFgIRAOTLXWrjfG3J97fBLwd2CaMWYtEqJ5wlq734d2hwzVq1fnzjvvLLFNnz59/GRNiJCcLAOiEeWcZlHeGDpI2OXNN2UFo6pVy3kSRSkfHvV4a+08YF6hfZPyPd8F6GwKxXmys2X1oPvuK/cpjMtUTNBPnZJZo3Fx5bZBUcqDTv1XQouNG+HEifIPiIJ46EWvLFg6OjCqOIgKuhJauIW0AoJeIQ+9ZUuIilJBVxxBBb0Q7qqIFaGoCozFsX37dmrUqEFsbCxt2rRh2LBhZGVllfieoqo59uzZk+Tk5ALndeetL1myhLvuuivv2Jw5c3jhhRdKvMbo0aNZtGiRR58hoEhOllmbrVqV/xwWTFgZC3q5MUa89KSk8l9fUcqJCnoAcP7555OamsratWtJS0vj448/9un1xo0bV2otmlGjRjF27Fif2uETkpMldh1W/q5dIQ8dRNA3bIBjxypwEkUpOyroHrB161b69u1LXFwc3bt3z6up8sUXX9C5c2c6dOjAVVddxZ49e85475QpU+jXrx+PP/44b7zxRt7+Z5555oySuOHh4XTq1ImdO3cCsHLlSq644gri4uK45pprSE9PL5f9VatWpV69eoDMOK1WrVpeKYKBAwfy/vvvA/D2228zdOhQAM4991wOHDjA7t27y3VNRzh1Clavrlj8HC8IeqdOMv1/1aoK2aEoZSVgy+cu+PMCdqd6V0zOjj2bvq/3LfP7Ro4cyaRJk2jVqhXLly/nwQcfZNGiRVx++eUsW7YMYwzvvPMO48aNY8KECXnve+utt/j666+ZM2cO6enpDBo0iEcffRSXy8WMGTNISkoiIyMjr31mZibLly/njTfeICsri1GjRvH555/TsGFDZs6cyTPPPMPUqVPLbH/Xrl3pmlt1MCEhgY4dO+Ydmzx5Mt26dSM6OpoJEyawbNmyvGMdO3YkISGBwYMHl/majrBunYh6BQW9QoOicHpgNCkJunevmC2KUgYCVtADhZIqLaalpXHLLbeQnp7OqVOniI6OzmvzwQcf0Lx5c+bMmUOVKlVo2bIlkZGRpKSksGfPHjp06EBkZCQZGRls3bqV2NhYfvnlF2666Sbat2/PunXrWLduXV4Oek5ODk2aNCnWzvJWeGzcuDEvvPACvXr14rPPPsur1Q5S4bFwkbCAxj2GUN4aLrlU2ENv1AjOOUcHRhW/E7CCXh5P2hfkr7RYmFGjRvGXv/yFAQMGsGTJEsaMGZN3rF27dqSmppKWlpYn9CNGjGDatGns3r2b4fkWXnDH0NPT0+nZsydz584lOjqatm3b8pO7rncplKXC4+HDhwvsC5kKj8nJ0KAB5PthLQ/GmiJ/DMvEpZeqoCt+R2PopVC3bt1iKy0ePnyYZs2k8OR7771X4H0dOnTg7bffZsCAAXlCeeONN7JgwQJWrFjBNddcc8a1mjRpwtixY3n55Zdp3bo1+/btyxP0rKws1q9fX6ydPXv25H//+5+7lg7vvfeeRxUek5KSmD9/PikpKYwfP55ff/0171jQVXhMTpZwSwXF2LhMxUIuIIK+bRscPFjBEymK56igF+L48eMFKie++uqrxVZaHDNmDEOGDKF79+5FesOXX34548eP59prr2X//v1UrVqVXr16cfPNNxMeXrRi3HDDDRw/fpzly5fzySef8MQTTxATE0NsbCyJ+VbDue+++/Js7NKlCyNHjqROnTrExMQQExPD0aNHGT169Bnn79GjBykpKVhrOXnyJPfeey9Tp06ladOmTJgwgeHDh2OtJSsriy1bthBf0Xi0v8jMlNmZFbTXWouxFQy5wGk7Vq6s4IkUpQwUV7XL11tlrLaYk5NjY2Ji7ObNmx2145FHHrHffPNNiW0+/fRT++yzzxZ5LCD/T8uWSYXF2bMrdJqc7Bw7hjH2/iH3V8yegwfFnpdeqth5FKUQVLDaouIFNmzYwAUXXEDv3r1pVZFJL17g6aef5vjx4yW2yc7O5rHHHvOTRV7APSBaUQ89J7fKYkW/GQ0awAUXnLZLUfxAwA6Khhpt2rRh27bAqCjcuHHjvMWviyN/Vk9QkJwMDRtCixYVOo0rRxbaMuEVHBQF+XHRRaMVPxJwHrq15ahDrfiNgP3/JCfLQGQFB0Sty0seOoig//Yb7N3rhZMpSukElKBXr16dAwcOBK5oVHKstRw4cIDq1as7bUpBjh2TqfZeGMB1h1zKXcslPzowqviZgAq5NG/enLS0NAJ9AenKTPXq1WnevLnTZhQkNVWm2ntB0L0acunQQe4YVq6Efv0qfj5FKYWAEvQqVaoUmG2pKB7hHnj0woISXhsUBahbF1q31oFRxW8EVMhFUcrFihXQtKlsFcSrHjrIj4wKuuInVNCV4Mc9Q9QL5MXQvSXo8fGwcyeUs1KmopQFFXQluDlyBDZtqnBBLjfuLBevDIqCDowqfkUFXQlu3DXHveSh54VcvCXosbGy2IaGXRQ/oIKuBDdeHBAFH4RcateGiy9WQVf8ggq6EtwkJ8O558osUS/g9UFRkLuH5GTQ+RWKj1FBV4IbLw6IgpcnFrmJj4c9e2RwVFF8iAq6ErwcPAhbt3pV0H3moYOGXRSfo4KuBC/uzBFveujeznIBiImB8HAVdMXnqKArwYuXB0TBB4OiADVqQLt2mrqo+BwVdCV4SU6WmuMNGnjtlD4JucDpGaM6MKr4EBV0JXjx8oAonPbQw8K9/NWIj4f9+6WcrqL4CBV0JTjZu1fE0cuC7jMPXQdGFT/gkaAbY/oaYzYZY7YYY54spk1PY0yqMWa9MeZ775qpKIXwwYAo5BsU9bagt28PVaqooCs+pdTyucaYcGAi0AdIA1YYY+Zaazfka1Mf+DfQ11r7mzGmkY/sVRQhOVlqjXfo4NXT5oVcwrx881qtGlxyiQq64lM86bWdgC3W2m3W2lPADGBgoTa3A59aa38DsNbqmluKb1mxQmqN163r1dP6LOQCOmNU8TmeCHoz4Pd8r9Ny9+XnQqCBMWaJMWalMWZYUScyxow0xiQbY5J1VSKlQrjXEPUyrmwRdK8PioII+qFDECCLhSuhhye9tihXpbCLEQHEAdcC1wB/NcZceMabrJ1srY231sY39FLtDaUSsmuX1Bf3cvwc/OChg4ZdFJ/hiaCnAS3yvW4O7CqizQJr7TFr7X7gByDGOyYqSiHcgugDQc/JzgF85KG3bSuxdBV0xUd40mtXAK2MMdHGmKrArcDcQm0+B7obYyKMMTWBzsBG75qqKLkkJ0uN8dhYr586J0cE3atT/91UrSplAFTQFR9RqqBba7OBh4GFiEh/bK1db4y53xhzf26bjcACYA2QBLxjrV3nO7OVSk1ysni7NWt6/dTukItPPHSQGaOrVoHL5ZvzK5WaUtMWAay184B5hfZNKvT6n8A/vWeaohSBtZLhcv31Pjm9T0MuIGGi//wHtmyBC88YZlKUCqEzRZXgYvt2mULfqZNPTu8OuYRF+FDQQcMuik9QQVeCi6QkefSRoLvTFn2S5QLQpg1Ur66CrvgEFXQluEhKOj3r0gf4POQSESGzW1XQFR+ggq4EF0lJ0LGj1EXxAa7cwUqvT/3PT3y8DIzmhncUxVuooCvBQ3a2FOXyUbgF8nnovoqhgwj6sWOwaZPvrqFUSlTQleBh/Xo4ccKngu7ztEXQgVHFZ6igK8GDjwdEwQ8xdJCiYrVqSfqlongRFXQleEhKkuXmzj/fZ5fwi4ceHi5e+vLlvruGUilRQVeCh6Qk8c6Nj1IKySfovoyhA1x2GaSmQmamb6+jVCpU0JXg4NgxWLfOp+EWyCfovsxyARH0rCxISfHtdZRKhQq6Ehy465/4WNDdM0XDI8J9eh06d5bHZct8ex2lUqGCrgQH7gFRHyxqkR+fLnCRnyZN4JxzNI6ueBUVdCU4SEqCc8+Fxo19ehm/DIq6uewy9dAVr6KCrgQHSUk+987Bj4OiIGGXHTtg927fX0upFKigK4FPerpUWezSxeeXcgt6eLiPY+ggHjpo2EXxGiroSuDz00/y2LWrzy/l15BLhw5Sk0bDLoqXUEFXAp/ERKmw2KGDzy/lVw+9Rg1ZRk8FXfESKuhK4JOYKDMrq1Xz+aVcOS5cxkWY8dNXo3NnKQGglRcVL6CCrgQ2J09KhUU/hFsgV9DDXBh8Nxu1AJddJpOm1q/3z/WUkEYFXQlsVq2CU6f8Jug222KN9Z+H7h4Y1bCL4gVU0JXAJjFRHv2Q4QKywIVfBf288yAqSgVd8Qoq6Epgk5go1RV9PKHIjTvk4jdBN0buPn780T/XU0IaFXQlcLFWBN1P4RYAm+PnkAtA9+7wyy+wZ4//rqmEJCroSuCyfbvMovSjoPvdQwe4/HJ5VC9dqSAq6Erg4o6f+1nQ/e6hd+woOekq6EoFUUFXApfERKhTB9q29dslbY71v4detarkoy9d6r9rKiGJCroSuPz0kwidP2Zt5mJdDsTQQeLoKSmQkeHf6yohhQq6EpgcOQKrV/s13AIOhVxA4ugul6YvKhVCBV0JTH78UQTuiiv8ellHQi4gE4zCwjTsolQIFXQlMPn+e4ktu2dS+gnHPPS6daVQlw6MKhVABV0JTJYskfVDa9b062Ud89BBwi7LlkmpA0UpBx71WmNMX2PMJmPMFmPMkyW0u9QYk2OMucl7JiqVjowMKcjVs6ffL+3IxCI33bvDiRMyOKoo5aDUXmuMCQcmAv2ANsBtxpg2xbR7BVjobSOVSkZCgpST9XP8HBzMcoHTE4w0jq6UE096bSdgi7V2m7X2FDADGFhEu1HAbGCvF+1TKiNLlshKPn4qyJUfR2aKujn7bLjgAvjhB/9fWwkJPOm1zYDf871Oy92XhzGmGXAjMKmkExljRhpjko0xyfv27SurrUpl4fvvZUHoWrX8fmlHQy4AvXrJ58/Odub6SlDjSa8tqtK/LfT6deAJa22Jy65Yaydba+OttfENGzb00ESlUnH0qKzg40D8HCTk4piHDtC7t+Tgr1zpzPWVoCbCgzZpQIt8r5sDuwq1iQdmGGMAooD+xphsa+0cbxipVCISEx2Ln0MAeOhXXimP330ns2QVpQx40mtXAK2MMdHGmKrArcDc/A2stdHW2pbW2pbAJ8CDKuZKuViyBCIi/D5D1I3jHnrDhtC+vQi6opSRUnuttTYbeBjJXtkIfGytXW+Mud8Yc7+vDVQqGd9/LwtC167tyOUd99BBwi4JCZLCqChlwKNea62dZ6290Fp7vrX2H7n7JllrzxgEtdbeZa39xNuGKpWAo0chKcmxcAsEkKCfPHm6fLCieIjOFFUChyVLJLujTx/HTHA85ALQo4eEnTTsopQRFXQlcFi4UKb6uyfYOEEOznvodepI2QMVdKWMqKArgcOCBZKuWK2aYyY4WsslP717Q3IyHDrkrB1KUKGCrgQG27bBli1wzTWOmmFtAMTQQQTd5ZJBYkXxEBV0JTBYmFsCqG9fZ+0IhJALSNngGjU07KKUCRV0JTBYuBBatoRWrRw1I2BCLtWqyeDo1187a4cSVKigK86TlQWLFkm4xRRVacJ/OFptsTD9+8OmTRKKUhQPCIBeq1R6fvpJaqA7HD8HIIfA8NABrr1WHr/6ylk7lKAhAHqtUulZuBDCw0/XMXGQgPLQzz8fLrpIBV3xmADotUqlZ+FCqX1er57TloArQAZF3Vx3nUy4yshw2hIlCAiQXqtUWvbsgVWrAiPcQoDMFM3PddfJGMO33zptiRIEBEivVSotX3wB1sL11zttiRAoaYtuunaVO5cvv3TaEiUI8KQeekBx6ugpju456rQZirf43zxo3h5qNYetB522BpsdYB56lSqSm//VVzLRKCxA7FICkqAT9F/m/8InN2sxx9AhRrZWbzltSB5ZVbICR9BBsl1mzpTQVHy809YoAUzQCXqzTs244f0bnDZD8QbLlsO/J8Izz0Dr1k5bA8BXm7/ix8wfA0vQ+/WT/Pwvv1RBV0ok6AS9/rn1qX9nfafNULzBvLHQMB2eHyxpiwHAvKXzOLboWGAJelSUZAF98QWMGeO0NUoAE0C9VqlUnDwpceGBAwNGzAFc1gUQWIIOMGiQhFy2bnXaEiWACbBeq1QaFi2S3Oobb3TakgIErKAPGSKPH3/srB1KQBNgvVapNHz2mawbGgCzQ/MTsIJ+zjkSdlFBV0ogwHqtUinIyYHPP5fiU9WrO21NAdyCbhwuElYkN98MqamwebPTligBigq64n+WLoW9ewMu3AIi6AHnnbtxh11mznTWDiVgCdCeq4Q0H3wg4ZYBA5y25AwCWtCbNZP1VjXsohRDgPZcJWQ5cQI++QQGD5YFoQOA7GzYuVOiGWm7XBgbxo8/wpo18NtvcDSQJibfcgusWwcbNjhtiRKABF0euhLkfPEFHDkCd97p90tbC+vXw7JlItZr1sj6EXv2yDEArnJB5zC6dy/43oYN4YILZEGlSy+VFeLat4eqVf38IQYPhkceES9dc9KVQhib15P9S3x8vE1OTnbk2oqDXH89pKTAjh1+yT8/eBDmzoX582HxYti3T/bXrg2XXAIXXwzNm0PTpiLa0/c+zlf7/s3czsc4cgQOH4b9+2UN619+gY0bYfduOUf16tCrl8zM798foqN9/nGEXr1g1y74+WfHV3hS/I8xZqW1tsgpw+qhK/5j3z5YsAD+8hefivmxYzBrFnz0kaS7Z2eLYF9zjWRJdu8O551XdJ2rhIUuqvwRxtVXF31uayEtTbz8H3+EefPg4Yfl2KWXwh13wK23QqNGPvt4MGwYDB8OiYnQrZsPL6QEGxpDV/zHjBmirj4Kt6SkwP33Q5MmcPfdshTnY49BUpKI8AcfyP4LLii+aGFpg6LGQIsWknDyxhvitW/eDP/8p3y0Rx+VH4/Bg+XHxCc3wEOGQJ068M47Pji5EsyooCv+44MPIDYW2rXz2ildLqlZ1asXdOwI778v2ZA//CCCPnaseM6eRibKk+XSqhWMHi0z89etkxuQ77+H3r2hbVt49104daocH644ateG226TOPrhw148sRLsqKAr/uHnn2HFCq955y6XhFUuuUTC8lu2wLhxElp+7z0Jq5QnvFzRtMW2bcWOtDSYNk3i7CNGiOj/+9+QmVnuUxdkxAg4flzuehQlFxV0xT9MniyLNQwdWqHTWCtVA2JjZeKkywX/+58MWj7+ONSvXzEzXdaFoeIDjdWrw5/+BCtXyoBs8+bw0EMycPrqq14Q9vh4SbPRsIuSD48E3RjT1xizyRizxRjzZBHHhxpj1uRuicaYGO+bqgQtJ06IuzpoEDRuXO7TfP01xMXJaU6ehOnTJcQxdKj8VngDb08sMkYWHPrxR8myadNG4vqtW8sPkctVgROPGAHJyZJAryh4IOjGmHBgItAPaAPcZoxpU6jZr8AV1tr2wN+Byd42VAliZs2CP/6QEctysGGDpAVecw0cOiQhlfXr4fbbvZ8s46uZosZAz57w3XeyRUVJ9Ck+vgLrP99xB1Srpl66kocnPbcTsMVau81aewqYAQzM38Bam2it/SP35TKguXfNVIIWa2HiRHFJr7iiTG/dt0/CFO3bS4be+PGSBz5sGET4KOHWH1P/r7xShhM+/FB+5/r0kVz2LVvKeKIGDeCmm8TVz8jwia1KcOFJz20G/J7vdVruvuK4B5hf1AFjzEhjTLIxJnmfe4aHEtosWyZ5g4884vEoZU6ODCC2agVvvy2O/S+/SKiiWjXfmuuvWi5hYZKo8vPP8kO1dKkMqD77rOTRe8yjj0qmi3rpCp4JelHfwiKza40xvRBBf6Ko49baydbaeGttfMOGDT23UgleXntNRiqHDfOoeVISdO4snnlcnEzPf+stmcXpD1z4tzhXtWryQ7Vpk5Rp+cc/ZPbq7Nke5rBfeqnc+bz2GmRl+dxeJbDxpOemAS3yvW4O7CrcyBjTHngHGGitPeAd85SgZscOUaZ775Xc6RI4eBAeeEBqpOzaJbM8v/1WBhH9iVPVFps0kRz6pUtPR1Kuvlo8+FJ5/HH4/Xetwqh4JOgrgFbGmGhjTFXgVmBu/gbGmHOAT4E7rbVafV8RJkyQ2MKoUcU2cbkkAaZ1a8lsfPRREbFbb3WmTInT5XMvv1xSHd96SxJY2reHp5+WlPNi6ddPfvnGjfPR1FQlWCi151prs4GHgYXARuBja+16Y8z9xhh32sJzQCTwb2NMqjFGq25VdvbuhSlTJJWjRYsim6xdK9GCu++WePnKlRI5qFvXz7bmw2lBBxnwfeghCcPcfju8/LLo9dy5xbwhLEymqq5ZA99841dblcDCo55rrZ1nrb3QWnu+tfYfufsmWWsn5T4fYa1tYK2Nzd2KrASmVCJef12SxZ84czjl2DHZ3bGjZK28+67kacfG+t3KMwgEQXfTqJHcvXz/vUSsBg6UNUG2by+i8e23S9zmn//0s5VKIBEYPVcJLfbvh3/9SwLBrVsXOPTll6enx//pT+KFDh9efLEsfxNIgu6mRw8pPPbPf0rBrzZt4KWXCtWHqVYN/t//k4GHpUsds1VxlsDquUpoMG6cuOH5FmBIS5MZntdfD7VqSfGsd96ByEjnzCyKQBR0kJmwo0fLHU2/fvDMMxATIwKfx0MPSanHJ57QWHolJfB6rhLcpKfLiN4dd0CbNmRnS/Tl4oulFPrLL4u3WXhFoEAhUAXdTYsWkjj01VfioffuLaUP0tORJf3GjIGffioh4K6EMoHbc5Xg5G9/k8Lgf/sbSUnQqZNEArp3l+n6Tz7pwLJtZSDQBd1N//5Sx+a552SJ1osukihXzrC7Jcz11FPyf1AqFYHfc5XgYc0aePddDt87modfO5/LLpP1OmfNEo/Sb0u0VYBgEXSAGjXg+edF2Dt3lsm4l3aJYPndkyQ28957Tpuo+Jng6LlK4GMt9i+PMb3GCC6a/SL/+Y+kn2/cKGOjwbL0ZTAJuptWrWDhQplXtGcPdHnqCu5v9CkHn30Vjh512jzFjwRXz1UClpRXvqb7d3/jjmNv07xFGElJskSbkznl5SEYBR3kB3PIEPkB/fOfDe8cuIHWu5cwbdBcHR+tRARfz1UCigMH4IF7ThH3VB82R7Thnckuli+XOizBSLAKupu6dWUBjZUrDa0aZ3D3N7fTo0MGa9c6bZniD4K35yqOkp19uiLilP+G8wj/YvOCX7nn3rCAySkvD8Eu6G5iYuDHn6N4t/5jbFyXTYcOltGjtcpuqBP8PVfxK9bK5KCYGEl7jj33IKk2htf/8hv1ewepW56PUBF0gLD6dRn+3hVsyrmAuzukMmHC6ZLEmgATmoRGz1X8wooV0KuXTA7KyoLZ7x/juwMdaHdhFrz4otPmeYVQEnQABgwg8qYrmbK2C8s++IVWraS+/CWXwOef6/yjUCOEeq7iK7Ztk8UYOnWS5eAmToT16yyD5t6FSd8FH3wgOXQhQMgJOsg/7Kyz6PzCtfzw5RHmzBEhv+EGKSuwbJnTBireIsR6ruJN0tLg4Ydl0srcufDXv8LWrfDgg1Dl3Ukyo+Uf/xClDxFCUtAbNYKZM2HbNsyIexg4wLJuHUyaJCtBdekiZRlWr3baUKWihFjPVbzB77+LaJ9/vsRb775bvvgvvAB16gAJCVK4vH9/KTASQoSkoINM1X3pJfkR/te/iIiA++6TdUyff15qwsTGirCnpjptrFJeQrDnKuXlt99k1aDzz5fCWXffLV/4t9+Wmk+AxF8GD4Zzz4Xp0wOnTKKXCFlBB/nxHTBAHnOretWuLeUDtm+XMjCLFkGHDhKOWbXKSWOV8hCiPVcpCxs2wIgRcMEFUpv8nntEyCdNEt3OY88eWRctK0tiMPXrO2WyzwhpQQ8Lk3IArVtLcfWVK/MO1a8vZXi2bxeP/fvvZS7B9dfLcx08DQ5CtOcqpWGteGPXXiv1yadPl6U/t2yB//wHzjmn0BsOH4a+faWs31dfSfnEECSkBR1EuRcskLrF/fpJLK3QYbfH/ve/y4Bpz56yFvWMGZruGOiEcM9ViuL4cZg6Vbyv3r0lFfH55yXcMnFiEUIOUg9k4ECpAjV7tqzkHKJYa0Nb0AGaNYOvv5Zf9auvlsW8C1GvHjz7rPSLSZNkQtJtt0k47pVXYN8+B+xWSiXEe67iZuNGGcds2lRCKidPypKfv/0mHlnDhsW8cf9+Uf6lS2VZ+r59/Wq3vwl5D93NhRfC/Pnwxx/QrZvUNi6CGjVk8HTjRomynXeelEBu3lzqsP/4o4ZjAolK0HMrLxkZ8N//yi1zmzYSSunXT2Ki69ZJ3Lx69RJOsGOHLEO/Zg189pm4aCFOpRF0gPh4+aF2uSQLJjGx2KZhYRJPX7xYtP+++2TGcPfuMklp/PjcRTYUR6kkPbfykJMjC7/fcQc0bizrde7aJRlrv/8OH30kk0lKLWebmAhdu8Lu3XJ7PmCAX+x3mkol6CBqnJgIUVFw1VXw4YelvqVNG3jzTelXU6ZIpszjj4vX3q+fxNpPnPCD7coZVKKeG7pkZYnm3nefhFSuvlrGLYcNk+/qpk2ygE3jxh6czFqYMAGuuEIWHl66NHDXi/MBlU7QAVq2lLkFcXESR7nvPsjMLPVttWrJXd6yZRKSefJJ8d5vu01CeLfcIjXatSS7H7HWOrLFxcVZpfycOGHtl19ae9dd1jZoYC1YW6uWtbfcYu2sWXK8zKSnWztggJzsxhut/eMPb5sd8LSd2NYOnjnYaTOcISvL2ieekP9/TIy169aV+RQ5OdZ+9521991nbaNGcqrq1a294QZrP/igUnYprwMk22J0NcLpHxTFM6wVT3vhQtmWLJHb2nr1JBoyeLB45uUqqeJyyUyiJ56QNJjXXpMR1GBZZsiLVEoP3U1EBIwdK+Mmd90lU0f/7/8k3cXDjhUWBldeKdvEieL4f/IJfPopzJkD4eGSJHXNNbLFxck+xTsY69AQdXx8vE1OTnbk2sFCerpEPL77TkTcnV124YWSbNKvn3xxKrTo8k8/yczBxEQZPZ00SSaeVFIueusiYs+OZcZNM5w2xVn275fA+LRpktoybpzUBSjnj7zLBUlJEgpcuBCSk8VJiYyEPn1k695dJrdVQj+iTBhjVlpr44s8poIeGFgrczyWLj29bdsmx+rUkczBvn3Fq2nZ0gsXXLVKqm3NmyfFm8aNk6B7Jf82XfivC4lvGs+Hg0sfHKwULF4she83bpSaAH//u9TwqWA/2b9fBu8XLBCB37NH9jduLDcI3bvLFhOjHnxhShJ0Dbk4gLXiba9cWXA7cECOR0VJp37oIXns0AGqVPHChXNy4Isv4K23xO1v0ABefllKKtau7YULBD+VOuRSFL16Sdrqhx9KsZfrroP27aXPDB0KNWuW67RRUTJ4ettt8n3YuFFy2t3OzOzZ0q5WLen/cXGyxcfLHaqKfNGoh+5jDhyQWinubf16qWbnFu+ICGjXTjpr587ilbRu7WVHeeNG+UJ+8IH8kjRvLuUUH3xQgvBKHue9cR6Xn3M579/4vtOmBB5ZWdKH3nhDRL5+fRH1226TGrxeLNT2++8i8D/9JM5OaqoM74CIfGysfG/atpU0yrZtxbuvDDeYGnLxMUePwq+/yrZtm9RDWb9eBHzv3tPtatWSEigxMeJpxMVJGnCJk3vKQ06OBCnnz5eRqNWrT49W3X+/TOOP0Juzomj5ekt6tuzJtBumOW1K4GKtqO3EibLsUWamVHG78UYJx/ToISmvXiQnB37++fTdbEqKfMcOHjzd5qyzRNgvuEBKFOTfzjrLq+Y4igp6BcjKkrk1u3ad3nbuLCjgheta1K0rXoN7c3sRzZv7qNrsyZMSE09MlO2HHyRIaYykFNx6K9x8M5x9tg8uHlqc89o5XHXeVUwdONVpU4KDI0dE1GfMkDDeyZMShuneXSamdekit55163r90tZK7H39+tPbhg2yCEvhWav164vQR0fL97B5cylp437epEkFkwv8iMbQ85GTI+UrDhwQzTtwoOC2f39B8d6378xaFeHh4pBER0vd6OhoSQRwP0ZG+ujWz1r5NXHHbtavlzn8KSlw6pS0iY4+nQJz9dUSrFQ8RmPoZaRuXbjzTtmOHZN82nnzJBA+Zoz0WWMkPnLJJeLduLfo6AoFw40RH+XssyVpID/HjomztXVrwW31asm0cYdv8p+rcWPZGjWSiVHFPUZGyscOxDi+R4JujOkLvAGEA+9Ya8cWOm5yj/cHjgN3WWt9Uh7/6FFZGu3IEalVkpFR+vMjR+TW7MABOHSo+GJCERHyz2raVH69L71UnhfeGjb08j/TWumB+X9ldu2SylnubccOecw/g69hQ/liPPLIaW9IvfAKoYJeAWrVknrM114rrw8fhuXLJRC+fLmIfP7SAhER4h6fe66U+XRvzZpJ327YUByS2rXL7CHVqiW/H5dccuYxa8W0tDTZdu48/XzPHnHitm6Vx4yM4q9Rt654/vXqyWP+zb2vTh0xv/DWuLFojbcpVdCNMeHARKAPkAasMMbMtdZuyNesH9Aqd+sM/Cf30et8+WXJNaLCw+WPWLeuPNapI3/c6Gj5A0ZFyeMZ21mWunUsxrokadZaceezsmQ7dUoeM07BwUL7Cj9mZopAu7ejR898nZFR8LbA7WEXpkkT6eQxMVId6bzzTsdxii2RqJQXFXQvUq+e3CVeffXpfUeOyCD9+vUy2OR2WL7/XpQ1J+fM81SrdvqL6/5S59/cX/aaNaWte6teveDr3H2mWjXqR0RQv3447SLDoWO4CId7i4jIe37iZBj79hv27ZPxsL175Q7/0CHZDh8+/fz33+WG2b3f5Sr+T/N//ydliL2NJx56J2CLtXYbgDFmBjAQyC/oA4H3c6elLjPG1DfGNLHWer3+WtYfr9Bs1DjCjItwXISRQxguwpDXxrr/ihYLHLFwBNhJrlt+IHdzv/bXEEJYGNQMg9ph0MTI6/DcjhNRJ7cDRUBEvk5VpUquZ5IBrM/dkL/8huIvpZSffcf3YagEqRJOUbeuxNQ7F+HvZWdL8HvnTnFy9u0r+HjggDhC+/fLAJb7VvzoUZ/V8K0BnBMWxjn5xT4sTL6XxW1VDK6oMI5Sm6OmDkdtLXme+5hha3PhiTjgbq/b64mgNwN+z/c6jTO976LaNAMKCLoxZiQwEuCcIldSKJ0Lzouiy7Y6yHfOlPzovk0zpvS2RT4aCDNgwuSfGJYrxO6tqP0mDMLDcn/lcwU6LBzViOCgbcO2DG0/1GkzKicREdCihWxlwR2yPHFCBmUzM+XRveV/7X6enS13A0VtpR1z38GXsIVZS93c7cz2GXB5Ld/8CT1oU5QUFf459KQN1trJwGSQLBcPrn0GXa65h1nX3FOetyqKEooYczo4XcnxJFiYBuT/yWwO7CpHG0VRFMWHeCLoK4BWxphoY0xV4FZgbqE2c4FhRrgMOOyL+LmiKIpSPKWGXKy12caYh4GFSNriVGvtemPM/bnHJwHzkJTFLUjaovej/YqiKEqJeJSHbq2dh4h2/n2T8j23wEPeNU1RFEUpC5pwqyiKEiKooCuKooQIKuiKoighggq6oihKiOBY+VxjzD5ghyMXrxhRwH6njfAz+plDn8r2eSF4P/O51toiCzk5JujBijEmubhaxKGKfubQp7J9XgjNz6whF0VRlBBBBV1RFCVEUEEvO5OdNsAB9DOHPpXt80IIfmaNoSuKooQI6qEriqKECCroiqIoIYIKegUwxow2xlhjTJTTtvgSY8w/jTE/G2PWGGM+M8bUd9omX2GM6WuM2WSM2WKMedJpe3yNMaaFMWaxMWajMWa9MeZRp23yF8aYcGNMijHmS6dt8RYq6OXEGNMCWTj7N6dt8QPfAO2ste2BzcBTDtvjE/ItiN4PaAPcZoxp46xVPicbeMxaezFwGfBQJfjMbh4FNjpthDdRQS8/rwH/h/+WmXYMa+3X1trs3JfLkBWpQpG8BdGttacA94LoIYu1Nt1auyr3eQYicM2ctcr3GGOaA9cC7zhtizdRQS8HxpgBwE5r7WqnbXGA4cB8p43wEcUtdl4pMMa0BDoAyx02xR+8jjhkLoft8CoeLXBRGTHGfAucXcShZ4Cngav9a5FvKenzWms/z23zDHKLPt2ftvkRjxY7D0WMMbWB2cCfrbVHnLbHlxhjrgP2WmtXGmN6OmyOV1FBLwZr7VVF7TfGXAJEA6uNMSDhh1XGmE7W2t1+NNGrFPd53Rhj/gRcB/S2oTt5oVIudm6MqYKI+XRr7adO2+MHugEDjDH9gepAXWPM/6y1dzhsV4XRiUUVxBizHYi31gZj1TaPMMb0BV4FrrDW7nPaHl9hjIlABn17AzuRBdJvt9aud9QwH2LEK3kPOGit/bPD5vidXA99tLX2OodN8QoaQ1c84S2gDvCNMSbVGDOptDcEI7kDv+4F0TcCH4eymOfSDbgTuDL3f5ua67kqQYh66IqiKCGCeuiKoighggq6oihKiKCCriiKEiKooCuKooQIKuiKoighggq6oihKiKCCriiKEiL8f1Imaj1vyBJOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "relu_deriv = 1.0*(activation_input > 0)\n",
    "leaky_deriv = 1.0*(activation_input > 0) + 0.3*(activation_input <= 0)\n",
    "\n",
    "sns.lineplot(x=activation_input, y=tanh_deriv, color='red', label=\"tanh'(x)\")\n",
    "sns.lineplot(x=activation_input, y=sigmoid_deriv, color='blue', label=\"$\\sigma'(x)$\")\n",
    "sns.lineplot(x=activation_input, y=relu_deriv, color='green', label=\"ReLU'(x)\")\n",
    "sns.lineplot(x=activation_input, y=leaky_deriv, color='purple', label=\"LeakyReLU'(x)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:41.613011Z",
     "start_time": "2021-03-25T03:26:41.610619Z"
    }
   },
   "outputs": [],
   "source": [
    "leak_rate = 0.1 #How much I want the LeakyReLU to \"leak\" by. Anything in [0.01, 0.3] would have been fine. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:41.621597Z",
     "start_time": "2021-03-25T03:26:41.614215Z"
    }
   },
   "outputs": [],
   "source": [
    "fc_relu_model = nn.Sequential(\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D,  n), nn.LeakyReLU(leak_rate),\n",
    "    *[nn.Sequential(nn.Linear(n,  n), nn.LeakyReLU(leak_rate)) for _ in range(5)], \n",
    "    nn.Linear(n, classes),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:41.626506Z",
     "start_time": "2021-03-25T03:26:41.623006Z"
    }
   },
   "outputs": [],
   "source": [
    "def cnnLayer(in_filters, out_filters=None, kernel_size=3):\n",
    "    \"\"\"\n",
    "    in_filters: how many channels are coming into the layer\n",
    "    out_filters: how many channels this layer should learn / output, or `None` if we want to have the same number of channels as the input.\n",
    "    kernel_size: how large the kernel should be\n",
    "    \"\"\"\n",
    "    if out_filters is None:\n",
    "        out_filters = in_filters #This is a common pattern, so lets automate it as a default if not asked\n",
    "    padding=kernel_size//2 #padding to stay the same size\n",
    "    return nn.Sequential( # Combine the layer and activation into a single unit\n",
    "        nn.Conv2d(in_filters, out_filters, kernel_size, padding=padding), \n",
    "        nn.LeakyReLU(leak_rate)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:26:41.635588Z",
     "start_time": "2021-03-25T03:26:41.627961Z"
    }
   },
   "outputs": [],
   "source": [
    "cnn_relu_model = nn.Sequential(\n",
    "    cnnLayer(C, n_filters), cnnLayer(n_filters), cnnLayer(n_filters),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    cnnLayer(n_filters, 2*n_filters), cnnLayer(2*n_filters), cnnLayer(2*n_filters), \n",
    "    nn.MaxPool2d((2,2)),\n",
    "    cnnLayer(2*n_filters, 4*n_filters), cnnLayer(4*n_filters),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D*n_filters//4, classes),\n",
    ")\n",
    "#Caption: This is our generic CNN code block. Ignoring the object name, this code block can be re-purposed for many different styles of CNN hidden layers by changing the definition of the cnnLayer function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:31:29.503401Z",
     "start_time": "2021-03-25T03:26:41.639058Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3af9fc2903e248348285ff70380f39e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20b4d0edf4eb4aed9aa5f7cc46707ea3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "fc_relu_results = train_network(fc_relu_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del fc_relu_model\n",
    "cnn_relu_results = train_network(cnn_relu_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del cnn_relu_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:31:29.771697Z",
     "start_time": "2021-03-25T03:31:29.505137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_results, label='FC')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_relu_results, label='FC-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_results, label='CNN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_relu_results, label='CNN-ReLU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:31:29.780103Z",
     "start_time": "2021-03-25T03:31:29.772970Z"
    }
   },
   "outputs": [],
   "source": [
    "fc_bn_model = nn.Sequential(\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D,  n), nn.BatchNorm1d(n), nn.LeakyReLU(leak_rate),\n",
    "    *[nn.Sequential(nn.Linear(n,  n), nn.BatchNorm1d(n), nn.LeakyReLU(leak_rate)) for _ in range(5)], \n",
    "    nn.Linear(n, classes),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:31:29.783965Z",
     "start_time": "2021-03-25T03:31:29.781204Z"
    }
   },
   "outputs": [],
   "source": [
    "def cnnLayer(in_filters, out_filters=None, kernel_size=3):\n",
    "    if out_filters is None:\n",
    "        out_filters = in_filters #This is a common pattern, so lets automate it as a default if not asked\n",
    "    padding=kernel_size//2 #padding to stay the same size\n",
    "    return nn.Sequential( # Combine the layer and activation into a single unit\n",
    "        nn.Conv2d(in_filters, out_filters, kernel_size, padding=padding), \n",
    "        nn.BatchNorm2d(out_filters), #The only change, adding BatchNorm2d after our convolution! \n",
    "        nn.LeakyReLU(leak_rate)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:31:29.793335Z",
     "start_time": "2021-03-25T03:31:29.785034Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "cnn_bn_model = nn.Sequential(\n",
    "    cnnLayer(C, n_filters), cnnLayer(n_filters), cnnLayer(n_filters),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    cnnLayer(n_filters, 2*n_filters), cnnLayer(2*n_filters), cnnLayer(2*n_filters), \n",
    "    nn.MaxPool2d((2,2)),\n",
    "    cnnLayer(2*n_filters, 4*n_filters), cnnLayer(4*n_filters),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D*n_filters//4, classes),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:36:45.674383Z",
     "start_time": "2021-03-25T03:31:29.794582Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96b1883c22ce4c83af5077e4c231d339",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dce22de0553c4034b15e024d78ed2bfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "fc_bn_results = train_network(fc_bn_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del fc_bn_model\n",
    "cnn_bn_results = train_network(cnn_bn_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del cnn_bn_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:36:46.023701Z",
     "start_time": "2021-03-25T03:36:45.675692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_relu_results, label='FC-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_bn_results, label='FC-ReLU-BN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_relu_results, label='CNN-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_bn_results, label='CNN-ReLU-BN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:36:46.032181Z",
     "start_time": "2021-03-25T03:36:46.025027Z"
    }
   },
   "outputs": [],
   "source": [
    "fc_ln_model = nn.Sequential(\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D,  n), nn.LayerNorm([n]), nn.LeakyReLU(leak_rate),\n",
    "    *[nn.Sequential(nn.Linear(n,  n), nn.LayerNorm([n]), nn.LeakyReLU(leak_rate)) for _ in range(5)], \n",
    "    nn.Linear(n, classes),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:36:46.037145Z",
     "start_time": "2021-03-25T03:36:46.033514Z"
    }
   },
   "outputs": [],
   "source": [
    "def cnnLayer(in_filters, out_filters=None, pool_factor=0,kernel_size=3):\n",
    "    if out_filters is None:\n",
    "        out_filters = in_filters #This is a common pattern, so lets automate it as a default if not asked\n",
    "    padding=kernel_size//2 #padding to stay the same size\n",
    "    return nn.Sequential( # Combine the layer and activation into a single unit\n",
    "        nn.Conv2d(in_filters, out_filters, kernel_size, padding=padding), \n",
    "        nn.LayerNorm([out_filters, W//(2**pool_factor), H//(2**pool_factor)]), #The only change, adding BatchNorm2d after our convolution! \n",
    "        nn.LeakyReLU(leak_rate)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:36:46.046217Z",
     "start_time": "2021-03-25T03:36:46.038352Z"
    }
   },
   "outputs": [],
   "source": [
    "cnn_ln_model = nn.Sequential(\n",
    "    cnnLayer(C, n_filters), \n",
    "    cnnLayer(n_filters), \n",
    "    cnnLayer(n_filters),\n",
    "    nn.MaxPool2d((2,2)), #we've done one round of pooling, so , pool_factor=1 now\n",
    "    cnnLayer(n_filters, 2*n_filters, pool_factor=1), \n",
    "    cnnLayer(2*n_filters, pool_factor=1), \n",
    "    cnnLayer(2*n_filters, pool_factor=1), \n",
    "    nn.MaxPool2d((2,2)), #now we've done two rounds of pooling, so pool_factor=2\n",
    "    cnnLayer(2*n_filters, 4*n_filters, pool_factor=2), \n",
    "    cnnLayer(4*n_filters, pool_factor=2),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D*n_filters//4, classes),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:42:00.432167Z",
     "start_time": "2021-03-25T03:36:46.047238Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41f0718ffbe04cd2b40a39298569487e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10dca82baaee486ba1ccc595e6830efe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "fc_ln_results = train_network(fc_ln_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del fc_ln_model\n",
    "cnn_ln_results = train_network(cnn_ln_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del cnn_ln_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:42:00.780037Z",
     "start_time": "2021-03-25T03:42:00.433408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_relu_results, label='FC-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_bn_results, label='FC-ReLU-BN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_relu_results, label='CNN-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_bn_results, label='CNN-ReLU-BN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_ln_results, label='FC-ReLU-LN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_ln_results, label='CNN-ReLU-LN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:42:00.787218Z",
     "start_time": "2021-03-25T03:42:00.781440Z"
    }
   },
   "outputs": [],
   "source": [
    "class SkipFC(nn.Module):\n",
    "    def __init__(self, n_layers, in_size, out_size, leak_rate=0.1):\n",
    "        \"\"\"\n",
    "        n_layers: how many hidden layers for this block of dense skip connections\n",
    "        in_size: how many features are coming into this layer\n",
    "        out_size: how many features should be used for the final layer of this block.  \n",
    "        leak_rate: the parameter for the LeakyReLU activation function. \n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        \n",
    "        #The final layer will be treated differently, so lets grab it's index to use in the next two lines\n",
    "        l = n_layers-1\n",
    "        #The linear and batch norm layers are stored in `layers` and `bns` respectively. A list comprehensive creates all the layers in one line each. The `if i == l` allows us to single out the last layer which needs to use `out_size` instead of `in_size`\n",
    "        self.layers = nn.ModuleList([nn.Linear(in_size*l, out_size) if i == l else nn.Linear(in_size, in_size) for i in range(n_layers)])\n",
    "        self.bns = nn.ModuleList([nn.BatchNorm1d(out_size) if i == l else nn.BatchNorm1d(in_size) for i in range(n_layers)])\n",
    "        #Since we are writing our own `forward` function instead of using nn.Sequential, we can just use one activation object multiple times. \n",
    "        self.activation = nn.LeakyReLU(leak_rate)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        #First we need a location to store the activations from every layer (except the last one) in this block. All the activations will be combined as the input to the last layer, which is what makes the skips! \n",
    "        activations = []\n",
    "        \n",
    "        #zip the linear and normalization layers into paired tuples, using [:-1] to select all but the last item in each list.\n",
    "        for layer, bn in zip(self.layers[:-1], self.bns[:-1]):\n",
    "            x = self.activation(bn(layer(x)))\n",
    "            activations.append( x )\n",
    "        #concatenate the activations together, this makes the input for the last layer\n",
    "        x = torch.cat(activations, dim=1)\n",
    "        #Now manually use the last linear and batch-norm layer on this concatenated input, giving us the result.\n",
    "        return self.activation(self.bns[-1](self.layers[-1](x)))\n",
    "#Caption: A PyTorch module defining a class for creating skip connections. It will create one larger block of multiple layers in a “dense” style skip connection, containing `n_layers` total. Used on its own it can create dense networks, or used sequentially it can create staggered skip connections. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:44:10.522503Z",
     "start_time": "2021-03-25T03:42:00.788654Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c5899a78eef4a3795c0a59c822524c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "fc_skip_model = nn.Sequential(\n",
    "    nn.Flatten(),\n",
    "    SkipFC(2, D, n),\n",
    "    SkipFC(2, n, n),\n",
    "    SkipFC(2, n, n),\n",
    "    nn.Linear(n, classes),\n",
    ")\n",
    "\n",
    "fc_skip_results = train_network(fc_skip_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del fc_skip_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:44:10.759046Z",
     "start_time": "2021-03-25T03:44:10.523752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_relu_results, label='FC-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_bn_results, label='FC-ReLU-BN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=fc_skip_results, label='FC-ReLU-BN-Skip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:44:10.774065Z",
     "start_time": "2021-03-25T03:44:10.761862Z"
    }
   },
   "outputs": [],
   "source": [
    "class SkipConv2d(nn.Module):\n",
    "    def __init__(self, n_layers, in_channels, out_channels, kernel_size=3, leak_rate=0.1):\n",
    "        super().__init__()\n",
    "        \n",
    "        #The last convolution will have a different number of inputs and output channels, so we still need that index\n",
    "        l = n_layers-1\n",
    "        #this is just simple helper values \n",
    "        f = (kernel_size, kernel_size)\n",
    "        pad = (kernel_size-1)//2\n",
    "        \n",
    "        #Defining the layers used, altering the construction of the last layer using the same `if i == l` list comprehension. We are going to combine convolutions via their channels, so the in and out channels change for the last layer.  \n",
    "        self.layers = nn.ModuleList([nn.Conv2d(in_channels*l, out_channels, kernel_size=f, padding=pad) if i == l else nn.Conv2d(in_channels, in_channels, kernel_size=f, padding=pad) for i in range(n_layers)])\n",
    "        self.bns = nn.ModuleList([nn.BatchNorm2d(out_channels) if i == l else nn.BatchNorm2d(in_channels) for i in range(n_layers)])\n",
    "        \n",
    "        self.activation = nn.LeakyReLU(leak_rate)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        #This code is identical to the SkipFC class, but its worth highliting the most important line that could change. \n",
    "        activations = []\n",
    "        \n",
    "        for layer, bn in zip(self.layers[:-1], self.bns[:-1]):\n",
    "            x = self.activation(bn(layer(x)))\n",
    "            activations.append( x )\n",
    "        #Which is the concatination of all the activations here. Our tensors are organized as (B, C, W, H), which is the default in PyTorch. But you can change that, and sometimes people use (B, W, H, C). In that situation the C channel is at index 3 instead of 1. So you would change `cat=3` in that scenario. This is also how you would adapt this code to work with RNNs\n",
    "        x = torch.cat(activations, dim=1)\n",
    "        \n",
    "        return self.activation(self.bns[-1](self.layers[-1](x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:47:58.646127Z",
     "start_time": "2021-03-25T03:44:10.775510Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43f2227b96ac4091b3e61e6e86a25ad1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "cnn_skip_model = nn.Sequential(\n",
    "    nn.Conv2d(C, n_filters, (3,3), padding=1), \n",
    "    SkipConv2d(3, n_filters, 2*n_filters),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    nn.LeakyReLU(),\n",
    "    SkipConv2d(3, 2*n_filters, 4*n_filters),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    SkipConv2d(2, 4*n_filters, 4*n_filters),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D*n_filters//4, classes),\n",
    ")\n",
    "\n",
    "cnn_skip_results = train_network(cnn_skip_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del cnn_skip_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:47:58.886812Z",
     "start_time": "2021-03-25T03:47:58.647470Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1BhZ2VzIDIgMCBSIC9UeXBlIC9DYXRhbG9nID4+CmVuZG9iago4IDAgb2JqCjw8IC9FeHRHU3RhdGUgNCAwIFIgL0ZvbnQgMyAwIFIgL1BhdHRlcm4gNSAwIFIKL1Byb2NTZXQgWyAvUERGIC9UZXh0IC9JbWFnZUIgL0ltYWdlQyAvSW1hZ2VJIF0gL1NoYWRpbmcgNiAwIFIKL1hPYmplY3QgNyAwIFIgPj4KZW5kb2JqCjEwIDAgb2JqCjw8IC9Bbm5vdHMgWyBdIC9Db250ZW50cyA5IDAgUgovR3JvdXAgPDwgL0NTIC9EZXZpY2VSR0IgL1MgL1RyYW5zcGFyZW5jeSAvVHlwZSAvR3JvdXAgPj4KL01lZGlhQm94IFsgMCAwIDM5Mi4xMzc1IDI2Mi4xODM3NSBdIC9QYXJlbnQgMiAwIFIgL1Jlc291cmNlcyA4IDAgUgovVHlwZSAvUGFnZSA+PgplbmRvYmoKOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDExIDAgUiA+PgpzdHJlYW0KeJydVk1vHDcMvc+v0LE9rCxSlEQe7X4YCAoDcbboudhunLh2msZpjf77Ps3O7GjG+1HXsOHZtxw96ol8Irn77uKS3N2TC+4ef8+O3LW7+H7798fN9vb6ym2eulXAF49dNPYUS8KHh+kDZzxofXqoge3HD133qcPaeOEay951XQq7l2LxSeoDVlXxNsMeJowTHobVxndbDAzvkT/v8r8DCfbgFbuolEC6nHxMKSu3nA0oPgyk3RVyfe6u1u7iR3IU3Pp9VzdTkplEQjCzt+TWv3XfhG/d+t79sO56so4ivjEOMttZi56mITavxDEVS4ieeHjOwyH4zCrBWp4WPcNjxYtoYUqG6IlHFjxZPWcpJc54GvQ0D0PgoAm/zIieePKcJ8bkLRDFPKuIBj3NE6EwNoMtaUb0xKMjzzytwD6GSJyceS5aH2r09vMfmw/zzMZiw3+lLCTIS7LPc+zI7vF9IUWkFB/IUlScK+0Kx6seZtLii5bM3DBN2DkmjSi1UKRgLR2YLBxmIhZfkmpsmCbsHBPh0TJ2lURDHqn4CFUmX2II0VquCTxLlgqMIFKOylFGMjlCZupLwE5KSzaBZ8kMxlLLjSlnHsnyYTI0q8+FJeWGrAHPkXEkPKakIaETB67DZRtgayuCmxHOqLdFIvYc+vJ97Ko51/e/bp++usvN5q8vv27+GRf6072w3IimUsdUrcB92bpf3CfH7o0jn6pveoI7ZM1cjd5LHn4K9l8CKgR7ZHO3yzui8VRUvaDttSpjBgPFozjcFxk1YwppGmckMi9V9Vp50B2tFWAaKDOYTTC4DfDG4QjurwnVVyVGTWATORmkKtFzAm3tlcap0KgeB1u0xkcYQFDG2w5+7nHSbHX9xnHIIo7QIvf3UDZfgFDVC1aTQgz90b7r3rpXS0tVT1PUPRSuegbIAX/IQU/qSSH6hHZZCEpCMDFG4S0UzXCNuNtwK2gpEKiY5Lmg9RGmGPsDaAVF48QMQOaCMpFHgcfe/hpB0TQwq9wv0+gJalh0zD1tq2eVSkIO8X/riejCg4Rd3U8R9JIGmX1zUtoiPqOPWBbK1vFDTZa1inFAs5nKUlpDiFnRRa3CvwUtHnUuLQfgSvUKnkvLOFEVsaW06AVkI4EW2kZ4RkKrLrWV5CkgRiZtq2xhPx3NBTkwlR0atrDSy2Ht8fCwhtj/OOs1kdMCx1e9uIy7Ye9NnSjx99xvaZwvy7ASxLPcX/CYw9DqlHUO4wC1thUH3HkNTuAdg+EEUYrxDJQxctM1MDxvTLJFzUcqlaElA7qLbRLbY5tpD3sMs2/yqGt0doMC28cNLBNUpuVGbMr8oQH3e2woJjUO6LmpQ/fVOHT33YiyOnN5uOOXR1cL0Ma8cSnWUaHMgf7kQ1/Bw5EP0/3sntQx2Qjn2t2p393crG63P/08XofHzNidNuN9fjAzpqGsykvwlXlyRMfup9Ax19XVzct0Z472Cq/bZ47rK0meJ77HXpl3MEzl+WXaq3e/f/w85v62+xeFZu8kCmVuZHN0cmVhbQplbmRvYmoKMTEgMCBvYmoKMTE0MAplbmRvYmoKMTYgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA4OCA+PgpzdHJlYW0KeJw1jLsNwDAIRHumuBH4OID3iVKR/dsQWy64e9IT5znAyD4PR+jELWRD4aZ4STmhPlE0Wm86tkhzv7xkMxcV/TPcfYXt5oBkNEnkIdHlcuVZKXo+l9obfgplbmRzdHJlYW0KZW5kb2JqCjE3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjU5ID4+CnN0cmVhbQp4nD1SSXLDMAy7+xV8AndJ70mnJ+f/1wL01JcQY4oACKZ3i8rZ+GkrWX7kxy47SyK3fAdtk/uKqgekboLMJcclV4hZyudKkFi0lJlYn6kOys81qFX4wk/MTEyHJBFLyEk9irB+Xgf3oH69pPNbZPyjY4PSW5JMBZ0a7pWjlNiwVJ8a5B403ZRWn5l2ZQcsjTdkpSZ1+vXT4wdLMKUvEtFBiMSf5HKHLAspB/F2rt5AeFe7INVTKw87RJqPkd5SBas5MwFLRtMOI2Bt1pggVQK8RWsItBbi8oVOKyI+OTd0ME1VnoXIsFLhWHvPaA0Xuoa80nB6knH/+R+8K97X7x/a82ZkCmVuZHN0cmVhbQplbmRvYmoKMTggMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMzIgPj4Kc3RyZWFtCnicNVE7cgUxCOt9Cl0gM+Zvn2czr0ru30awk2ZhAQkJ5z3YiMSXGNId5YpvWZ1mGX4ni7z4WSmcvBdRgVRFWCHt4FnOaobBcyNT4HImPsvMJ9NixwKqiTjOjpxmMAgxjetoOR1mmgc9IdcHI27sNMtVDGm9W6rX91r+U0X5yLqb5dYpm1qpW/SMPYnLzuupLe0Lo47ipiDS4WOH9yBfxJzFRSfSzX4z5bCSNASnBfAjMZTq2eE1wsTPjARP2dPpfZSG1z5our53L+jIzYRM5RbKSMWTlcaYMVS/Ec0k9f0/0LM+f5owVEcKZW5kc3RyZWFtCmVuZG9iagoxOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDU5ID4+CnN0cmVhbQp4nDM1NVcwULC0ABKmpkYK5kaWCimGXEA+iJXLZWhpDmblgFkWxkAGSBmcYQCkwZpzYHpyuNIAqeEQWgplbmRzdHJlYW0KZW5kb2JqCjIwIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggNzUgPj4Kc3RyZWFtCnicNY2xEcAwCAN7pmAEywET9smlwvu3CfhopBccyOTmwZ6ydLBN5wf056RN80JRkKow0HRmfXFo5A5WDhdeaEqviujPQe8HmeoXmgplbmRzdHJlYW0KZW5kb2JqCjIxIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjI3ID4+CnN0cmVhbQp4nEWQS44DIRBD95zCR6D+cJ6OsurcfzsuOtFssCUo1zO5AxN78chMlG68ZLg7zBWf4Rkwc/hKmGzETOhOXCOUrhThVJ8IjsvevOmgiXtEzqOeBVnVzg1qAWeS5oLtgi7njBU3zsmtRuXN9KPXEL5pdx/XeYf2SOPew1S+zjnVzruKCGkLWdW0vpBsFMkOaz8qTdvOyxCx4GwaVugc3gi7V3cnSxh+v/IwJRM/D936UXxdN6PrFGcnVyZrz3noSelf9cqjD8VxKegXse3MJPdfp1OSqVN7Z+9p/ae4x/sPkG5WOQplbmRzdHJlYW0KZW5kb2JqCjIyIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzM4ID4+CnN0cmVhbQp4nDVSS5JbQQjbv1PoAq5q/s15nJrV5P7bCOysoIEWEpAWOMjESwxRjXLFH3mC8TqBv+vlafw+3oXUgqci/cC1aRvvx5o1UbA0YinMPvb9KCHHU+PfEOi5SBNmZDJyIBmI+7U+f9abTDn8BqRpc/ooSXoQLdjdGnZ8WZBB0pMaluzkh3UtsLoITZgbayIZObUyNc/HnuEynhgjQdUsIEmfuE8VjEgzHjtnLXmQ4XiqFy9+vY3XMo+pl1UFMrYJ5mA7mQmnKCIQv6AkuYm7aOoojmbGmtuFhpIi9909nJz0ur+cRAVeCeEs1hKOGXrKMic7DUqgauUEmGG99oVxmjZKuFPT7V2xr99nJmHc5rCzUjINznFwL5vMESR73TFhEx6HmPfuEYzEvPldbBFcucy5JtOP/SjaSB8U1+dcTZmtKOEfquSJFdf4//zez88/kDd9sQplbmRzdHJlYW0KZW5kb2JqCjIzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTUzID4+CnN0cmVhbQp4nDWPyw3EMAhE765iGliJryH1ZJVTtv/rgp1IRjzZM2MIFRAy8GHFNEfIgS+Pumn6Dc656F4kVQ9RVhHYGSIJ5sA5xCdSoSSYE1pV5xxWAQUW3q9O+shdeQW47cDVsx1N+9t3qE0S9hBHYkr5KeB160fbJCoa5rkW6gG612ClaBKyFrDHNjDTSqh9O7BaK893/3tcf/J4NoYKZW5kc3RyZWFtCmVuZG9iagoyNCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMwNCA+PgpzdHJlYW0KeJw9kjuSwzAMQ3udghfIjPiT5PNkJ5X3/u0+MslWgEmJACgvdZmypjwgaSYJ/9Hh4WI75XfYns3MwLVELxPLKc+hK8TcRfmymY26sjrFqsMwnVv0qJyLhk2TmucqSxm3C57DtYnnln3EDzc0qAd1jUvCDd3VaFkKzXB1/zu9R9l3NTwXm1Tq1BePF1EV5vkhT6KH6UrifDwoIVx7MEYWEuRT0UCOs1yt8l5C9g63GrLCQWpJ57MnPNh1ek8ubhfNEA9kuVT4TlHs7dAzvuxKCT0StuFY7n07mrHpGps47H7vRtbKjK5oIX7IVyfrJWDcUyZFEmROtlhui9We7qEopnOGcxkg6tmKhlLmYlerfww7bywv2SzIlMwLMkanTZ44eMh+jZr0eZXneP0BbPNzOwplbmRzdHJlYW0KZW5kb2JqCjI1IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjMwID4+CnN0cmVhbQp4nDVRSW7DMAy86xXzgQDiLr/HQU/t/68d0glgYGhLnM0RGxsReInBz0HkxlvWjJr4m8ld8bs8FR4Jt4InUQRehnvZCS5vGJf9OMx88F5aOZMaTzIgF9n08ETIYJdA6MDsGtRhm2kn+oaEz45INRtZTl9L0EurEChP2X6nC0q0rerP7bMutO1rTzjZ7aknlU8gnluyApeNV0wWYxn0ROUuxfRBqrOFnoTyonwOsvmoIRJdopyBJwYHo0A7sOe2n4lXhaB1dZ+2jaEaKR1P/zY0NUki5BMlnNnSuFv4/p57/fwDplRTnwplbmRzdHJlYW0KZW5kb2JqCjI2IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjQ1ID4+CnN0cmVhbQp4nEVQu41DMQzrPQUXCGD9LHued0iV2789SkZwhSFaP5JaEpiIwEsMsZRv4kdGQT0LvxeF4jPEzxeFQc6EpECc9RkQmXiG2kZu6HZwzrzDM4w5AhfFWnCm05n2XNjknAcnEM5tlPGMQrpJVBVxVJ9xTPGqss+N14GltWyz05HsIY2ES0klJpd+Uyr/tClbKujaRROwSOSBk0004Sw/Q5JizKCUUfcwtY70cbKRR3XQydmcOS2Z2e6n7Ux8D1gmmVHlKZ3nMj4nqfNcTn3usx3R5KKlVfuc/d6RlvIitduh1elXJVGZjdWnkLg8/4yf8f4DjqBZPgplbmRzdHJlYW0KZW5kb2JqCjI3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzkyID4+CnN0cmVhbQp4nD1SS24FMQjbzym4QKXwTXKeqd7u3X9bm8xUqgovA7YxlJcMqSU/6pKIM0x+9XJd4lHyvWxqZ+Yh7i42pvhYcl+6hthy0ZpisU8cyS/ItFRYoVbdo0PxhSgTDwAt4IEF4b4c//EXqMHXsIVyw3tkAmBK1G5AxkPRGUhZQRFh+5EV6KRQr2zh7yggV9SshaF0YogNlgApvqsNiZio2aCHhJWSqh3S8Yyk8FvBXYlhUFtb2wR4ZtAQ2d6RjREz7dEZcVkRaz896aNRMrVRGQ9NZ3zx3TJS89EV6KTSyN3KQ2fPQidgJOZJmOdwI+Ge20ELMfRxr5ZPbPeYKVaR8AU7ygEDvf3eko3Pe+AsjFzb7Ewn8NFppxwTrb4eYv2DP2xLm1zHK4dFFKi8KAh+10ETcXxYxfdko0R3tAHWIxPVaCUQDBLCzu0w8njGedneFbTm9ERoo0Qe1I4RPSiyxeWcFbCn/KzNsRyeDyZ7b7SPlMzMqIQV1HZ6qLbPYx3Ud577+vwBLgChGQplbmRzdHJlYW0KZW5kb2JqCjI4IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggOTAgPj4Kc3RyZWFtCnicTY1BEsAgCAPvvCJPUETQ/3R60v9fq9QOvcBOAokWRYL0NWpLMO64MhVrUCmYlJfAVTBcC9ruosr+MklMnYbTe7cDg7LxcYPSSfv2cXoAq/16Bt0P0hwiWAplbmRzdHJlYW0KZW5kb2JqCjI5IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTYzID4+CnN0cmVhbQp4nEWQuXUEMQxDc1WBEniAOuoZP0ez/acLabzeQPp4hHiIPQnDcl3FhdENP962zDS8jjLcjfVlxviosUBO0AcYIhNXo0n17YozVOnh1WKuo6JcLzoiEsyS46tAI3w6ssdDW9uZfjqvf+wh7xP/KirnbmEBLqruQPlSH/HUj9lR6pqhjyorax5q2r8IuyKUtn1cTmWcunsHtMJnK1f7fQOo5zqACmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA1MiA+PgpzdHJlYW0KeJwzNjNUMFAwsVQwMjZRMDY0AmIThRRDLqAIiJXLBRPLAbNAqnK4oMpzYKpyuNIA6QkNwAplbmRzdHJlYW0KZW5kb2JqCjMxIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggNjggPj4Kc3RyZWFtCnicMzK3UDBQsDQBEoYWJgrmZgYKKYZcQL6piblCLhdIDMTKAbMMgLQlnIKIW0I0QZSCWBClZiZmEEk4AyKXBgDJtBXlCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA4MSA+PgpzdHJlYW0KeJw9zLsVgDAIBdA+U7wRQnyA7OOx0v1bwUQbuHzVAx0hGdQNbh2HtKxLd5N96nq1iaTIgNJTalwaToyoaX2pfWrguxvmS9WJP83P5wOHxxlrCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMjAgPj4Kc3RyZWFtCnicNVG7ccUwDOs1BRfwnfiVNI9zr8rbvw1AOxVhGgRAqrxkSrlc6pJVssLkR4fqFE35PmCm/A71kOPoHtkhulPWlnsYCMvEPKWOWE2We7gFgS8MTYm5hfP3COgrBqMwE4G6xd8/QLMkMGlw8FOQa61aYokOPCwWWLMrzK0aKVTIVXw7NrkHBXJxs9CnHJoUt9yC8GWIZEdqsa/LZSnyu/UJGIQV5ohPFImF54EOZiLxJwNie/bZYldXL6oRGdZJhwdSBNJsbhIwNEWy6oMb2FfHNT9PR9nByUG/isH4NjiZL0l5XwWhEI8X/g7P2cixkkMkFPJ9tcCII2yAEaFP7SMQZSA0RffumVI+JlWK7wBGIRx9qlcyvBeR2WqGzf8ZXdkqCgZVWR+fRnAmg0k482SjCtNStdO/+9zj8wdjY3qACmVuZHN0cmVhbQplbmRvYmoKMzQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMTQgPj4Kc3RyZWFtCnicPVC7EUMxCOs9BQvkznztN8/Lpcv+bSScpEI2QhKUmkzJlIc6ypKsKU8dPktih7yH5W5kNiUqRS+TsCX30ArxfYnmFPfd1ZazQzSXaDl+CzMqqhsd00s2mnAqE7qg3MMz+g1tdANWhx6xWyDQpGDXtiByxw8YDMGZE4siDEpNBv+tcvdS3O89HG+iiJR08K755fTLzy28Tj2ORLq9+YprcaY6CkRwRmryinRhxbLIQ6TVBDU9A2u1AK7eevk3aEd0GYDsE4njNKUcQ//WuMfrA4eKUvQKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIzNiA+PgpzdHJlYW0KeJxNUEtuRCEM23OKXOBJJCEBzkPVVef+27HDVO0qhhh/SA/pslUe61NidYns8qVNl8oyeRWo5U/b/1EMAm7/0MhBtLeMnWLmEtbFwiQ85TQjGyfXLB+PO08bZoXGxI3jnS4ZYJ8WATVblc2BOW06N0C6kBq3qrPeZFAMIupCzQeTLpyn0ZeIOZ6oYEp3JrWQG1w+1aEDcVq9Crlji5NvxBxZocBh0Exx1l8B1qjJslnIIEmGIc59o3uUCo2oynkrFcIPk6ER9YbVoAaVuYWiqeWS/B3aAjAFtox16QxKgaoAwd8qp32/ASSNXVMKZW5kc3RyZWFtCmVuZG9iagozNiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDQ5ID4+CnN0cmVhbQp4nDM2tFAwUDA0MAeSRoZAlpGJQoohF0gAxMzlggnmgFkGQBqiOAeuJocrDQDG6A0mCmVuZHN0cmVhbQplbmRvYmoKMzcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNTcgPj4Kc3RyZWFtCnicRZC5EUMxCERzVUEJErAI6rHH0Xf/qRf5SrRvAC2HryVTqh8nIqbc12j0MHkOn00lVizYJraTGnIbFkFKMZh4TjGro7ehmYfU67ioqrh1ZpXTacvKxX/zaFczkz3CNeon8E3o+J88tKnoW6CvC5R9QLU4nUlQMX2vYoGjnHZ/IpwY4D4ZR5kpI3Fibgrs9xkAZr5XuMbjBd0BN3kKZW5kc3RyZWFtCmVuZG9iagozOCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMzMiA+PgpzdHJlYW0KeJwtUjmOJDEMy/0KfmAA6/Lxnh5M1Pv/dElVBQWqbMs85HLDRCV+LJDbUWvi10ZmoMLwr6vMhe9I28g6iGvIRVzJlsJnRCzkMcQ8xILv2/gZHvmszMmzB8Yv2fcZVuypCctCxosztMMqjsMqyLFg6yKqe3hTpMOpJNjji/8+xXMXgha+I2jAL/nnqyN4vqRF2j1m27RbD5ZpR5UUloPtac7L5EvrLFfH4/kg2d4VO0JqV4CiMHfGeS6OMm1lRGthZ4OkxsX25tiPpQRd6MZlpDgC+ZkqwgNKmsxsoiD+yOkhpzIQpq7pSie3URV36slcs7m8nUkyW/dFis0UzuvCmfV3mDKrzTt5lhOlTkX4GXu2BA2d4+rZa5mFRrc5wSslfDZ2enLyvZpZD8mpSEgV07oKTqPIFEvYlviaiprS1Mvw35f3GX//ATPifAEKZW5kc3RyZWFtCmVuZG9iagozOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMxNyA+PgpzdHJlYW0KeJw1UktyQzEI279TcIHOmL99nnSyau6/rYQnK7AtQEIuL1nSS37UJdulw+RXH/clsUI+j+2azFLF9xazFM8tr0fPEbctCgRREz34MicVItTP1Og6eGGXPgOvEE4pFngHkwAGr+FfeJROg8A7GzLeEZORGhAkwZpLi01IlD1J/Cvl9aSVNHR+Jitz+XtyqRRqo8kIFSBYudgHpCspHiQTPYlIsnK9N1aI3pBXksdnJSYZEN0msU20wOPclbSEmZhCBeZYgNV0s7r6HExY47CE8SphFtWDTZ41qYRmtI5jZMN498JMiYWGwxJQm32VCaqXj9PcCSOmR0127cKyWzbvIUSj+TMslMHHKCQBh05jJArSsIARgTm9sIq95gs5FsCIZZ2aLAxtaCW7eo6FwNCcs6Vhxtee1/P+B0Vbe6MKZW5kc3RyZWFtCmVuZG9iago0MCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE3ID4+CnN0cmVhbQp4nDM2tFAwgMMUQy4AGpQC7AplbmRzdHJlYW0KZW5kb2JqCjQxIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTMxID4+CnN0cmVhbQp4nEWPyw0EIQxD71ThEvIZPqmH1Z7Y/q/rMJpBQvhBIjvxMAis8/I20MXw0aLDN/421atjlSwfunpSVg/pkIe88hVQaTBRxIVZTB1DYc6YysiWMrcb4bZNg6xslVStg3Y8Bg+2p2WrCH6pbWHqLPEMwlVeuMcNP5BLrXe9Vb5/QlMwlwplbmRzdHJlYW0KZW5kb2JqCjQyIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjQ4ID4+CnN0cmVhbQp4nC1ROZIDQQjL5xV6QnPT77HLkff/6QrKAYOGQyA6LXFQxk8Qlive8shVtOHvmRjBd8Gh38p1GxY5EBVI0hhUTahdvB69B3YcZgLzpDUsgxnrAz9jCjd6cXhMxtntdRk1BHvXa09mUDIrF3HJxAVTddjImcNPpowL7VzPDci5EdZlGKSblcaMhCNNIVJIoeomqTNBkASjq1GjjRzFfunLI51hVSNqDPtcS9vXcxPOGjQ7Fqs8OaVHV5zLycULKwf9vM3ARVQaqzwQEnC/20P9nOzkN97SubPF9Phec7K8MBVY8ea1G5BNtfg3L+L4PePr+fwDqKVbFgplbmRzdHJlYW0KZW5kb2JqCjQzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTcxID4+CnN0cmVhbQp4nE2QTQ5CIRCD95yiFzCh8wOP82hc6f23dvD54oL0SyFDp8MDHUfiRkeGzuh4sMkxDrwLMiZejfOfjOskjgnqFW3BurQ77s0sMScsEyNga5Tcm0cU+OGYC0GC7PLDFxhEpGuYbzWfdZN+frvTXdSldffTIwqcyI5QDBtwBdjTPQ7cEs7vmia/VCkZmziUD1QXkbLZCYWopWKXU1VojOJWPe+LXu35AcH2O/sKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDEzOCA+PgpzdHJlYW0KeJw9j0EOAzEIA+95hT8QKXZCWN6zVU/b/19Lmt1e0AiMMRZCQ2+oag6bgg3Hi6VLqNbwKYqJSg7ImWAOpaTSHWeRemI4GNwetBvO4rHp+hG7klZ90OZGuiVogkfsU2nclnETxAM1Beop6lyjvBC5n6lX2DSS3bSykms4pt+956nr/9NV3l9f3y6MCmVuZHN0cmVhbQplbmRvYmoKNDUgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMTAgPj4Kc3RyZWFtCnicNVDLDUMxCLtnChaoFAKBZJ5WvXX/a23QO2ER/0JYyJQIeanJzinpSz46TA+2Lr+xIgutdSXsypognivvoZmysdHY4mBwGiZegBY3YOhpjRo1dOGCpi6VQoHFJfCZfHV76L5PGXhqGXJ2BBFDyWAJaroWTVi0PJ+QTgHi/37D7i3koZLzyp4b+Ruc7fA7s27hJ2p2ItFyFTLUszTHGAgTRR48eUWmcOKz1nfVNBLUZgtOlgGuTj+MDgBgIl5ZgOyuRDlL0o6ln2+8x/cPQABTtAplbmRzdHJlYW0KZW5kb2JqCjE0IDAgb2JqCjw8IC9CYXNlRm9udCAvRGVqYVZ1U2FucyAvQ2hhclByb2NzIDE1IDAgUgovRW5jb2RpbmcgPDwKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ1IC9oeXBoZW4gL3BlcmlvZCA0OCAvemVybyA1MCAvdHdvIDUyIC9mb3VyIDU0IC9zaXggNTYgL2VpZ2h0Ci9uaW5lIDY1IC9BIC9CIC9DIDc2IC9MIDc4IC9OIDgyIC9SIC9TIDg1IC9VIDk3IC9hIDk5IC9jIDEwMSAvZSAxMDQgL2ggL2kKMTA3IC9rIDExMSAvbyAvcCAxMTQgL3IgL3MgL3QgL3UgMTIxIC95IF0KL1R5cGUgL0VuY29kaW5nID4+Ci9GaXJzdENoYXIgMCAvRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250RGVzY3JpcHRvciAxMyAwIFIKL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0gL0xhc3RDaGFyIDI1NSAvTmFtZSAvRGVqYVZ1U2FucwovU3VidHlwZSAvVHlwZTMgL1R5cGUgL0ZvbnQgL1dpZHRocyAxMiAwIFIgPj4KZW5kb2JqCjEzIDAgb2JqCjw8IC9Bc2NlbnQgOTI5IC9DYXBIZWlnaHQgMCAvRGVzY2VudCAtMjM2IC9GbGFncyAzMgovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TmFtZSAvRGVqYVZ1U2FucyAvSXRhbGljQW5nbGUgMAovTWF4V2lkdGggMTM0MiAvU3RlbVYgMCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL1hIZWlnaHQgMCA+PgplbmRvYmoKMTIgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTUgMCBvYmoKPDwgL0EgMTYgMCBSIC9CIDE3IDAgUiAvQyAxOCAwIFIgL0wgMTkgMCBSIC9OIDIwIDAgUiAvUiAyMSAwIFIgL1MgMjIgMCBSCi9VIDIzIDAgUiAvYSAyNCAwIFIgL2MgMjUgMCBSIC9lIDI2IDAgUiAvZWlnaHQgMjcgMCBSIC9mb3VyIDI4IDAgUgovaCAyOSAwIFIgL2h5cGhlbiAzMCAwIFIgL2kgMzEgMCBSIC9rIDMyIDAgUiAvbmluZSAzMyAwIFIgL28gMzQgMCBSCi9wIDM1IDAgUiAvcGVyaW9kIDM2IDAgUiAvciAzNyAwIFIgL3MgMzggMCBSIC9zaXggMzkgMCBSIC9zcGFjZSA0MCAwIFIKL3QgNDEgMCBSIC90d28gNDIgMCBSIC91IDQzIDAgUiAveSA0NCAwIFIgL3plcm8gNDUgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNCAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9DQSAwIC9UeXBlIC9FeHRHU3RhdGUgL2NhIDEgPj4KL0EyIDw8IC9DQSAxIC9UeXBlIC9FeHRHU3RhdGUgL2NhIDEgPj4KL0EzIDw8IC9DQSAwLjggL1R5cGUgL0V4dEdTdGF0ZSAvY2EgMC44ID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8ID4+CmVuZG9iagoyIDAgb2JqCjw8IC9Db3VudCAxIC9LaWRzIFsgMTAgMCBSIF0gL1R5cGUgL1BhZ2VzID4+CmVuZG9iago0NiAwIG9iago8PCAvQ3JlYXRpb25EYXRlIChEOjIwMjEwMzI0MjM0NzU4LTA0JzAwJykKL0NyZWF0b3IgKE1hdHBsb3RsaWIgdjMuMy4yLCBodHRwczovL21hdHBsb3RsaWIub3JnKQovUHJvZHVjZXIgKE1hdHBsb3RsaWIgcGRmIGJhY2tlbmQgdjMuMy4yKSA+PgplbmRvYmoKeHJlZgowIDQ3CjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAwMDAxNiAwMDAwMCBuIAowMDAwMDExNzMyIDAwMDAwIG4gCjAwMDAwMTE0OTUgMDAwMDAgbiAKMDAwMDAxMTUyNyAwMDAwMCBuIAowMDAwMDExNjY5IDAwMDAwIG4gCjAwMDAwMTE2OTAgMDAwMDAgbiAKMDAwMDAxMTcxMSAwMDAwMCBuIAowMDAwMDAwMDY1IDAwMDAwIG4gCjAwMDAwMDAzOTYgMDAwMDAgbiAKMDAwMDAwMDIwOCAwMDAwMCBuIAowMDAwMDAxNjExIDAwMDAwIG4gCjAwMDAwMTAwODkgMDAwMDAgbiAKMDAwMDAwOTg4OSAwMDAwMCBuIAowMDAwMDA5NDA5IDAwMDAwIG4gCjAwMDAwMTExNDIgMDAwMDAgbiAKMDAwMDAwMTYzMiAwMDAwMCBuIAowMDAwMDAxNzkyIDAwMDAwIG4gCjAwMDAwMDIxMjQgMDAwMDAgbiAKMDAwMDAwMjQyOSAwMDAwMCBuIAowMDAwMDAyNTYwIDAwMDAwIG4gCjAwMDAwMDI3MDcgMDAwMDAgbiAKMDAwMDAwMzAwNyAwMDAwMCBuIAowMDAwMDAzNDE4IDAwMDAwIG4gCjAwMDAwMDM2NDQgMDAwMDAgbiAKMDAwMDAwNDAyMSAwMDAwMCBuIAowMDAwMDA0MzI0IDAwMDAwIG4gCjAwMDAwMDQ2NDIgMDAwMDAgbiAKMDAwMDAwNTEwNyAwMDAwMCBuIAowMDAwMDA1MjY5IDAwMDAwIG4gCjAwMDAwMDU1MDUgMDAwMDAgbiAKMDAwMDAwNTYyOSAwMDAwMCBuIAowMDAwMDA1NzY5IDAwMDAwIG4gCjAwMDAwMDU5MjIgMDAwMDAgbiAKMDAwMDAwNjMxNSAwMDAwMCBuIAowMDAwMDA2NjAyIDAwMDAwIG4gCjAwMDAwMDY5MTEgMDAwMDAgbiAKMDAwMDAwNzAzMiAwMDAwMCBuIAowMDAwMDA3MjYyIDAwMDAwIG4gCjAwMDAwMDc2NjcgMDAwMDAgbiAKMDAwMDAwODA1NyAwMDAwMCBuIAowMDAwMDA4MTQ2IDAwMDAwIG4gCjAwMDAwMDgzNTAgMDAwMDAgbiAKMDAwMDAwODY3MSAwMDAwMCBuIAowMDAwMDA4OTE1IDAwMDAwIG4gCjAwMDAwMDkxMjYgMDAwMDAgbiAKMDAwMDAxMTc5MiAwMDAwMCBuIAp0cmFpbGVyCjw8IC9JbmZvIDQ2IDAgUiAvUm9vdCAxIDAgUiAvU2l6ZSA0NyA+PgpzdGFydHhyZWYKMTE5NDkKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABJ5ElEQVR4nO3deXhN1/rA8e/KJCJBRkOCJIIIkphirGqpoVSvVhWdqBYt2v463M7T7e2s7dXptqiqUjOXGlqtDqpaRZAgc0xBJmSek/X7Yx9pEBzk5GR4P89zHjl7fE+w37PX2utdSmuNEEIIcT4bawcghBCiZpIEIYQQolKSIIQQQlRKEoQQQohKSYIQQghRKTtrB1CVPDw8tK+vr7XDEEKIWmP37t3pWmvPytbVqQTh6+vLrl27rB2GEELUGkqpIxdbJ01MQgghKiUJQgghRKUkQQghhKhUneqDqExxcTFJSUkUFBRYOxRRzRwdHfHx8cHe3t7aoQhRK9X5BJGUlISLiwu+vr4opawdjqgmWmtOnTpFUlISfn5+1g5HiFqpzjcxFRQU4O7uLsmhnlFK4e7uLneOQlyDOp8gAEkO9ZT8vQtxbepFghBCiLqotKyUX479wheRX1jk+JIgqkFycjLjxo2jbdu2BAUFcfPNNxMbG4tSio8++qh8uxkzZrBgwQIAJk6ciLe3N4WFhQCkp6dzsVHir7zyCt7e3oSGhhIUFMSSJUsuG5Ozs/M57w8fPkznzp0vOO6sWbOu4JMKIarDmYIzfBH5BSPWjGDmTzNZHrOcotKiKj+PJAgL01ozevRoBg4cSEJCAgcPHuSNN94gJSUFLy8vZs+eTVFR5X+xtra2zJ8/36zz/N///R979+5l7dq1TJ06leLi4qr8GEKIGiAyLZLntz3P4BWD+U/4f2jp3JL3rn+P9betx8HWocrPJwnCwn7++Wfs7e2ZNm1a+bLQ0FBatWqFp6cngwYN4quvvqp038cee4wPPviAkpISs8/Xrl07nJycOHPmDADvvvsuPXv2JDg4mJdffvnaPowQotoVlhayNn4t49aPY8LGCfx45EdGtxvNmlFrmD90PkN8h2BvY5lHuev8Y64VvfrtAQ6eyKrSYwa1bMzLt3S66Pr9+/fTvXv3i65/5plnGD58OPfff/8F61q3bk3//v35+uuvueWWW8yKJzw8nHbt2uHl5cXmzZuJi4vjr7/+QmvNqFGj2Lp1KwMGDDDrWEII60nKTmJ57HLWxK0hozAD/yb+PNfrOW7xvwVnB+fLH6AK1KsEURP5+fkRFhbGN998U+n65557jlGjRjFixIhLHueDDz5g7ty5JCYm8t133wGwefNmNm/eTNeuXQHIyckhLi6u0gRxsSd+5EkgIapPmS5j+4ntLI1eytakrdgoG25odQPjAscR1jys2v8/1qsEcalv+pbSqVMnVq5cecltnnvuOcaMGVPphTsgIIDQ0FCWL19evuz5559nw4YNAOzduxcw+iCefPJJVq9ezb333ktCQgJaa5599lmmTp162Tjd3d3Lm6XOOn36tAwyE6IaZBZm8r/4/7E8ZjlHs4/i7ujOg8EPckf7O2jeqLnV4pI+CAu78cYbKSwsZO7cueXLdu7cyZEjf1fYDQwMJCgoiPXr11d6jOeff/6cp4lef/119u7dW54cKrrtttvo0aMHX331FUOHDmX+/Pnk5OQAcPz4cVJTUys9h7OzMy1atGDLli2AkRy+++47+vfvf8WfWQhhnqhTUby8/WUGrxjMrF2zcG/oztvXvc0PY35gZteZVk0OYOE7CKXUMGA2YAvM01q/dd56V2A+0BYoAO7XWu83rfs/4AFAA5HAJK11rRsWq5RizZo1PPbYY7z11ls4Ojri6+vLf/7zn3O2e/7558ubgs7XqVMnunXrRnh4uFnnfOmll5gwYQJRUVFERUXRp08fwEgCixYtwsvLi7y8PHx8fMr3efzxx1m4cCHTp0/niSeeAODll1+mbdu2V/GphRAXU1RaxA9HfmBJ9BL2pe2joV1DRviPYFzgOALdAq0d3jmU1toyB1bKFogFbgKSgJ3AeK31wQrbvAvkaK1fVUoFAp9orQcppbyBbUCQ1jpfKbUc2Ki1XnCpc/bo0UOfP2FQVFQUHTt2rMqPJmoR+fsXNUVybjLLY5azKm4VpwtO09qlNeMCx3FrwK00dmhstbiUUru11j0qW2fJO4gwIF5rnWgKYilwK3CwwjZBwJsAWutopZSvUqpZhdgaKqWKASfghAVjFUKIKqe1ZkfyDpZGL+XnYz+jteZ6n+sZFziOPi37YKOusZVfa8g4AplJ4Fv1zcGWTBDewLEK75OAXudtsw+4DdimlAoD2gA+WuvdSqlZwFEgH9istd5c2UmUUlOAKWA8FiqEENaWXZTNuoR1LItZxqHMQzRt0JSJnSYytsNYvJ29r/7AuelwPBxOhMPx3cYr7xQ0dIN/JkIVP+VkyQRRWaTnt2e9BcxWSu3F6GfYA5SY+iZuBfyADGCFUupurfWiCw6o9RxgDhhNTFUWvRBCXKG4M3EsjV7Kt4nfkl+STxePLrze/3WG+g6lgW2DKztYUR6c3Pd3Iji+27hbAECBZyC0Hw7e3cD74mOtroUlE0QS0KrCex/OaybSWmcBkwCU8YDvIdNrKHBIa51mWrca6AtckCCEEMKaisuK2XJ0C0ujl7I7ZTcONg4M9xvO+MDxdPIw89H60hJIizIlgnDjlXoQdKmxvkkrIxH0nGwkgxYh0MDFch/KxJIJYifQTinlBxwHxgETKm6glGoK5GmtizCeWNqqtc5SSh0FeiulnDCamAYB5/Y+CyFENSkpKyGnKIfs4myyi4xXTlEO0WeiWRW7irT8NLydvXm8++OMDhhNU8emFz/Y2X6D8mSwG07shZJ8Y71jEyMJdHjc+LNlN3BpdvHjWZDFEoTWukQpNQP4HuMx1/la6wNKqWmm9Z8BHYGFSqlSjM7ryaZ1O5RSK4FwoASj6WmOpWIVQtRdWmsKSwvLL+xnL/I5RTlkFWUZPxfnkF2UTVZRlpEITMvOrs8/e/GuRH/v/rwS+Ar9WvbD1sb2wg0u1m8AYNvAuBvoPtFIBt7dwM2/yvsSrpZFx0ForTcCG89b9lmFn/8A2l1k35eBOlFdLjk5mccee4ydO3fSoEGD8nEQHTp04MMPP2TmzJmAUe67R48eTJw4kYkTJ/LDDz+QmJhIgwYNSE9Pp0ePHhw+fPiC47/yyivMnTsXT09PioqKePHFFxk/fvwlY3J2di4fQAdGue+RI0eyf//+c47r7OzMk08+ec6+CxYs4KmnnsLb25vi4mI6duzIwoULcXJy4pVXXuGdd97h8OHDeHl5VXouIa5Fen46B08dJKMw4++LfoWL/PnLsoqyKCm7dMFLO2WHi4MLzg7OuDi44OLggkdDj/KfnR2caezQ2PjZ3timsUNj3Bu649HQ4+8DXWm/QbNOYFtz50yvV6U2rOFsue/77ruPpUuXAkZ5jIrlvqdOnYqDw4Wles+W+37ooYcue56zpTbi4uLo3r07Y8aMwd7ecv/w7rzzTj7++GMAJkyYwLJly5g0aRIAHh4evPfee7z99tsWO7+oH0rKSog7E8fetL3sS9vH3tS9HM85fsF2De0a4mL/9wXe1dGV1i6tz7noN3ZoXH5xP//laOt45XWOysog9QAcXF+j+g2qkiQIC7tYue/Dhw/j6elJv379+Oqrr3jwwQcv2Pdsue/K1l1MxXLfXl5evPvuuyxfvpzCwkJGjx7Nq6++WiWf66ySkhJyc3NxdXUtX3b//fezYMECnn76adzc3Kr0fKJuyyjIICI9gr2pRkKITI8sb97xbOhJqFco4wPH08WjS/k3fGcHZ4uVu75AaQkc3Q4H10H0esg+aSx3bGrqNxhm9X6DqlS/EsSmZyA5smqP2bwLDH/roqvrarnvZcuWsW3bNk6ePEn79u3Pic/Z2Zn777+f2bNnV3lCEnVHmS4jMSPRuDMw3SEcyjwEgK2ypYNbB0YHjCbEM4RQr1BaNGphnerCJUVw6FeIWgfRG4z+A7uGEDAIAkdAq141qt+gKtWvBFED1dZy32ebmLTWTJ8+nXfffZdnnnmmfP0jjzxCaGhoeV0nIXKKcohMjyxPBhFpEWQXZQPQtEFTQj1DGdV2FCGeIXRy74STvZP1gi3Oh/gtRlKI+Q4KM8HBBdoPhY63QLubwKGR9eKrJvUrQVzim76l1IVy38eOHSu/Q5g2bRqOjo7l2yiluOWWW/joo4/OSRBNmzZlwoQJfPrpp5c9t6h7tNYcyz5mJINU4w4h7kwcGo1CEeAawFDfoYR6hhLiGUKbxm2sP/dIYTbEfm8khbgfoDjPaDrqOBI6jgL/gWDveLmj1Cn1K0FYwY033shzzz3H3Llzy/sSdu7cSV5eXvk2Fct9h4WFXXCM559//pw7iNdff53XX3+90vPddtttfPXVV+Xlvl988UXuuusunJ2dOX78OPb29uVPF1VUsdz3oEGDyst9P/roo7Rq1eqc0uILFiw4Z99t27ZVWvX18ccfp2fPnlc0ZaqonfJL8jmQfqC8uSgiLYLTBacBcLZ3JtgzmMGtBxPiGUIXzy64ONSQztq80xCzyUgKCT9DaSE08oKQcUZS8O1fo58ysjRJEBZWV8t9n+2DKCsrw8fH54KkAcbTTKNHj+aDDz4wK25RO2itSc5NLk8Ge1P3EnM6hhJtfBHwbezLdd7XEeIVQqhnKP5N/CsfH2AtOalGB/PBdXD4NygrgcY+xtNGHW8x+hRqUrxWZLFy39Yg5b7F+eTvv2qU6TJ2nNzB/+L/x66UXaTmGRNPNbRrSGePzkZHsmcowZ7BuDq6XuZoVpCZBFHfGknh6B+ANjqWO46CoFHGU0fWbuKyEmuV+xZC1HIZBRmsTVjLitgVHMk6QpMGTejbsq/Rd+AVQnvX9tX3iOmVOpVgNB0dXGeMYgbwCoLrnzaSgldQvU0K5pIEIYQ4h9aafWn7WB6znO8Pf09RWRGhnqFM7T+VIb5DrrwqaXXRGlKjjDuFqHWQYqoK0LIrDHrZuFvwCLBujLWMJAghBAC5xblsSNzAsphlxJ6JpZF9I0a3G80d7e+gg1sHa4dXOa3h5F7jLiFqHZyKBxS07g1D3zD6FJrKPDFXSxKEEPVczOkYlscsZ33ievJK8gh0C+SlPi9xs9/NNLKvgc/6F2YbA16j1ht3C5lHQdkaTxz1fggCR4JLc2tHWSdIghCiHiooKWDzkc0sj1nOvrR9NLBtwFDfodzZ4U66eHSx/pgErSEnBdJiID22wisOsky1mGwdwP8GGPg0dLgZnKSsS1WTBCFEPXIk6wjLY5azNmEtmYWZ+Db25akeT3FrwK00adCk+gMqLYYzhyskgjhIjzH+LMz6ezsHF/BoB77XGX96BoLfAHBsXP0x1yPXOGO2MEdycjLjxo2jbdu2BAUFcfPNNxMbG4tSio8++qh8uxkzZpSPJ5g4cSLe3t4UFhYCkJ6ejq+vb6XHf+WVV/D29iY0NJSgoCCWLFly2ZicnZ3PeX/48GE6d+58wXFnzZp1wb4LFizA09OT0NBQOnXqxJgxY8oH/r3yyis4OTmRmpp60XOdlZKSwsiRIwkJCSn/vQD88ssvjBw58oLt161bx1tvVf9o+NquuKyYH478wAObH2DkmpF8E/UNvZr34oshX7DuH+u4t9O9lk8OBVlG6et9S+HHV2HpXfBxGLzeAj7uAcvugi2vQuLP4OAMwXfC8Hfh3rXweBQ8ewym/Ay3fQ4DnjRGN0tysDi5g7AwKfd9cS+99BI33XQTjz76KAARERGX3H7UqFGMGjWqCqKvH5Jzk1kRu4LVcatJz0+nRaMWzOw6k9EBo/F08qz6E2oN2cl/3wGkx5ruDOIgu8JswzZ2xhgEj/ZGsTuP9qZXO7noX4WC4lIS0nLo1LLqk7wkCAuTct8Xbxc+efIkQ4YMKX8fHBx8wTY7d+5kypQprFq1iq1bt7Jr1y4+/vhjJk6ciKOjIwcOHCAlJYX333+/0ruO+qa0rJTtJ7azPGY5W49vRWtNf+/+3NnhTvp796+aEc2lxXD6kKlJyJQAziYCU/E9wGgW8mwP/tcbF3+PDkYicPOr1+UrqorWmvURJ3lrUzQFxaVse/pGGjpU7QjwepUg3v7rbaJPR1fpMQPdAnk67OmLrpdy3xdPSNOnTy+/Exk8eDCTJk2iZcuW5eu3b9/OzJkzWbt2La1bt2br1q3n7H/48GF+/fVXEhISuOGGG4iPjz+nkGB9cir/FGvi17AydiXHc47j5ujG5M6Tub397Xg7e1/7CXJSYd8SiFgBaVFGeYqzXFoaiSB0fIW7gfbGk0TW7uyuo/YcPcNr6w8SfjSDwOYuvH17cJUnB6hnCaImqs/lvocOHVoe76ZNm+jatWv5lKdRUVFMmTKFzZs3n5M0Kho7diw2Nja0a9cOf39/oqOjCQ0Nvej56hqtNbtTdrM8Zjk/HP2BkrISejbvyWPdH2NQq0HYX+u39NISiP8R9nwNsd8ZScEnDPo9+neTkEf7WjdLWm12PCOfd76LZu3eE3g4N+Dt27swpnsrbG0sk4jrVYK41Dd9S5Fy33+X+/7kk0+YO3cuABs3bqRly5a4ubkxYcIEJkyYwMiRI9m6dSvu7u60aNGCgoIC9uzZc9EEcX7ysvqjmdUkqyiLbxO+ZUXMChIyE3BxcGFch3Hc0eEO/Jv4X/sJTifCnkWw9xtjxjQnD2N8Qdd7wLOGDpir43IKS/jvL/HM+82YUGnGDQFMG9gW5waWvYTXqwRhDVLu++9y39OnT2f69Onl63/66Sd69+6Nk5MT2dnZJCQk0Lp1a3Jzc2natClffPEFQ4YMoVGjRgwcOPCC469YsYL77ruPQ4cOkZiYSIcOdfvidSD9AMtjl7Pp0CbyS/Lp4tGFf/X9F8P8htHQruG1Hbw43xiNvOdro8KpsoGAwTD8HWg/DOwufIhCWF5pmWbFrmPM2hxLek4h/whtyVPDAvFueo1/32aSBGFhUu774uW+d+/ezYwZM7Czs6OsrIwHHniAnj178ssvvwDQrFkzvv32W4YPH878+fMv2L9Dhw5cf/31pKSk8Nlnn9XZ/of96ft5+6+32Zu2l4Z2DbnZ72bGdhhLkHvQtR/8xF4IXwiRK41Z01x94cYXIGQCNKmCvgtx1X6PT+e19QeJTs6mextX5t7bna6tq7dSrpT7FrXSxIkTGTlyJGPGjLnkdrX57/90wWk+DP+Q1XGrcXN048HgBxnVdtS1T7aTf8bobN6z0ChZYedoFLLrdg+06Q82MjzKmuJTc3hzYxRbolPxcW3Is8M7cnOX5hZrQpVy30LUIiVlJayIXcFHez4ivzife4LuYVrItGtLDGVlcHgrhH9t1C8qLYTmwXDzLOgyBhrWwDkc6pkzuUXM3hLHoj+P4GhvyzPDA5nY1xdHe+tNXiQJQtRKlTVp1QW7U3bzxo43iD0TS68WvXg27FnaNq28mc8smUlGZ/OeRZBxBBybQLd7jbuFFiFVF7i4akUlZSz84zAfbokjp7CE8WGt+b+b2uPhbP2y6vUiQWit680TLuJvtan5NDUvlfd2vcfGQxtp3qg5713/Hje1uenq/t2WFEHsJqNvIeEn0GVG3aIbXzRKVNhXTwenuDStNd8fSOGtTVEcPpXHgPaevDCiI+2b1ZzHhut8gnB0dOTUqVO4u7tLkqhHtNacOnWqxndcF5cWsyhqEZ/t+4zismKmBE9hcufJONk7XfnBUqONp5D2LYW8dGMA23VPQOhdxuhlUWPsP57Ja+sPsuPQadp5ObNgUk8Gdrjw6UJrs2iCUEoNA2YDtsA8rfVb5613BeYDbYEC4H6t9X7TuqbAPKAzoE3r/rjSGHx8fEhKSiItLe1aPoqohRwdHc95Uqum2X58O2/+9SaHsw4z0Gcg/+z5T1o1bnVlBynMhv2rjcSQtNOoc9RhOHS9FwIGQVWU1hBVJjmzgHe/j2H1niRcnRx47R+dGd+zFXa2NfPBAIslCKWULfAJcBOQBOxUSq3TWh+ssNlzwF6t9WilVKBp+0GmdbOB77TWY5RSDsBVfKUCe3t7/Pzk25O4CqUl8OcncOwvY7RwAxdo0NgoKFf+c5MLlzu4XPJJoKTsJN7d+S4/HfuJ1i6t+WTQJwzwuYLyJ1obMe1ZCPvXQHGuUedoyL8heBw4W6AQn7gmeUUlzNmayOe/JlJappkywJ/pNwTQ2LFm16Sy5B1EGBCvtU4EUEotBW4FKiaIIOBNAK11tFLKVynVDMgHBgATTeuKgCILxirEudJiYc1UY7J79wCjXb8w0/jGrssus7OqNKEUODjzJWf4Iv8INsqGRz17c2/zAThkZ8Ghrca2DVxMSafxhYPTctKMekh7vjYK5dk3gs63GZ3OPj2l7lENVFamWbPnOO9+H0NyVgEjurTg6WGBtHa/qu+71c6SCcIbOFbhfRLQ67xt9gG3AduUUmFAG8AHKAXSgC+VUiHAbuBRrXWuBeMVwngcdMdnxtwE9k5wxwLoNPrv9VpDUa4xmU1htjHPwdnEUZB13nLjpQsy+angJO8WZ3PcBobmFfBk+imaJyYCyy8WCdg2MCWXxuDgBKmmInmtesGoj424GlQ+14awvh2Jp/j3higij2cS4tOEjyd0pYdv7Zr1zpIJorKvM+c/VvIWMFsptReIBPYAJYA90A2YqbXeoZSaDTwDvHjBSZSaAkwBo/qpEFftzGH433Q4sg3aD4dbZoNLs3O3Ucq4KJt5YT6UeYi3/3qb308cJqBpAF+EPUtYizDTHUm2kVwKTEmlMKvCz+ctL8wGv+uNuwWph1SjHTmVy5sbo/nuQDItmjjynztDGRXSEhsLFdSzJEsmiCSgYo+bD3Ci4gZa6yxgEoAyHjE6ZHo5AUla6x2mTVdiJIgLaK3nAHPAGEldhfGL+kJrCP8Kvn/eqEF066cQOuGammxyi3P5POJzvj74NY62jjzd82nuDLwTextTm7OdA9i5QyP3KvoQwtoy84v5+Kc4Fmw/jL2tDU/c1J4HrvO3SBnu6mLJBLETaKeU8gOOA+OACRU3MD2plGfqY3gA2GpKGllKqWNKqQ5a6xiMjuuDCFHVsk7CupkQ/4MxVuDWT6HpFT5JVIHWmo2HNvLervdIy0/jHwH/4NFuj+LR0KMKgxY1SXFpGUv+OsoHP8SSkV/M2O6teGJIe7wa1+xHrM1hsQShtS5RSs0Avsd4zHW+1vqAUmqaaf1nQEdgoVKqFCMBTK5wiJnAYtMTTImY7jSEqBJaGwXqNj4JJYXG/Mc9H7imOkQxp2N4Y8cbhKeGE+QexAc3fECIp4xWrqu01vwSk8a/NxwkIS2XPv7uvDCyo0Wm/rSWOl+sT4gL5KbD+v+DqHXGBDijPwP3qy9nkVmYySd7P2FZzDIaOzTmkW6PcFvAbVUzvaeoUbTWRCdnsyHiJBsjT5KYnou/RyOeu7kjgzp61crBuFKsT4izojfAt49CQSYMfgX6PnLVg8nKdBlr4tYwO3w2mUWZjG0/lhldZ9CkQd35Bin+TgobI0+yIcJICjYK+rR1Z+r1/ozu6oODXc0c6HatJEGI+iE/A757FvZ9A827wL1roVmnqz5cZFokb+x4g/2n9tPVqyvP9XqOQLfAqotXWJXWmpiUbDZGnGR95EkS04yk0NvfncnX+TG0U/MaUUzP0iRBiLov4WdYOx2yk2HAP2HAU1c9Q9qp/FPMDp/Nmvg1eDT04I3+bzDSf2StbFoQ59JaE5uSw4bIk2yIOEGCKSn08nPn/n5+DOtcP5JCRZIgRN1VlAs/vAQ754FHe5j8A/h0v6pDlZSVsCxmGZ/s+YT8knwmdprI1OCpODvIQLXaLjbF6FPYEHmS+NSc8qQwsZ8fwzo1x9OlfiWFiiRBiLrp6J+wZpox+K33dBj04lWXud6ZvJM3drxBfEY8fVr04Zlez+DfxL9q4xXVKi4lm/Wmjua41ByUgl5+btzXt3O9TwoVSYIQdUtxAfzyBvz+oTGeYeJ68O1/RYdIy0tjX9o+ItIi2JO6h71pe2nZqCX/Gfgfbmx9ozQn1VJxKdmm5qO/k0KYrxuv3dqJoZ2b4+VS+8ctVDVJEKLuOLHXuGtIi4LuE43qpg0uPflKUWkR0aejyxPCvrR9nMw9CYCdjR1BbkHM7DqTe4LuoaGdTLRT28SnZrMhIpkNkSeITTGSQk9fN/51ayeGdWpeJwazWZIkCFH7lRbDb+/D1nfAyQPuWgntbqp00+TcZPal7StPCFGnoigqMwoFN2/UnBDPEO7ueDchXiEEugXSwFaaGmqb+NSc8kdSY1KyjaTQxo1XR3VieGdJCldCEoSo3VKj4X/T4MQe6HIHDH8HnIyKmQUlBUSdjmJf6j4i0o27g9S8VAAa2Dagk3snJnScQLBnMMEewTRr1OxSZxI1WEJaTvngtejkv5PCK7cEMbxLC5pJUrgqkiBE7VRWCn9+ClteA4dG6DELON6mJxHJf5bfHUSfiaakrAQAb2dvejTrQbBnMKGeobR3bY+9bc2erEVcWqIpKWwwJQWAnr6uvHxLEMM7t6B5E0kK10oShKh9Th8i73/TOJC6hwi/YPZ5tSXiwGxO7T4FQEO7hnT26Mx9QfcR4hlCF88uUiyvjtBasyHyJJ/+nMDBk1kA9GgjScFSJEGIGk9rzdHso0Sk7mPfwWVEpIQTa29LaYtmUJqCb25D+nn3I8QzhGDPYAKaBmBnI/+065rt8em89V00EUmZtPNy5qWRQQzv0pwWTeThAUuR/0WiRjqWdYxNhzeVNxdlFGYA0KisjC4OLkxu/w9CWvUn2COYpo5NrRqrsKz9xzN5+7tofotLp2UTR2bdEcLort7Y1sIJeGobSRCiRknKTmJOxBzWJayjVJfStklbbmjUhpCUEwQXFOI/8EVsez54TWW5Re1w9FQeszbHsG7fCZo62fPCiI7c3bsNjvZSJbe6SIIQNcKJnBPMiZjD2vi12CgbxgeO536/W/H86d8Q9a0xD/Pd/72mstyidkjPKeTjn+JZvOMItjaK6Te0ZcqAtjRpKA8VVLfLJgil1CzgS631gWqIR9QzybnJzIucx6q4VSgUY9vfwWSvXngdC4cvRxjzMd/0L+gz46rLcovaIaewhHm/JTJ3ayIFJWXc2bMVjw5qJ4+oWpE5dxDRwByllB3wJbBEa51p2bBEXZeal8q8yHmsjF2J1prb3UJ4IL+M5lvnQd7bxkat+8KI96BZkHWDFRZVVGJM2fnhljhO5RYxvHNznhzagbaeUgjR2i6bILTW84B5SqkOGNN+Riilfgfmaq1/tnSAom5Jz0/ni4i5LI9ZTpku5R8lDjx4MpGWiYnGKOiAwcbL/wZw9rR2uMKCyso030ac4L3NsRw9nUdvfzfmDQuka2tXa4cmTMzqg1BK2QKBplc6sA94XCk1VWs9zoLxiTriVOoBvtw5i2XpuynWZYzKyWVKZg4+LbrDgDuNpNA8RDqf64nf4tJ4a1M0B05kEdjchQWTenJ9e08phFjDmNMH8T4wCtgCvKG1/su06m2lVIwlgxO1WEkhHP2DM7Eb+PLYFpbaFVKoFCMLNdO8+tAq7Bbwux4aNrV2pDXemdwivvz9EKVa0y/Ag+5tXGlgVzv7YyKSMnj7u2h+jz+Fj2tDPrgzhFtDvLGRR1ZrJHPuIPYDL2it8ypZF1bF8Yja7HQixG+B+B/JOPIbXznZ8U1jF/IdbBjh0p6pXWfg63sjyLdEsxQUlzL/90P895cEcgtLUErxyc8JONrbEObnznUBHvQL8CCwuUuNv8AeSs9l1uYYNkScxK2RAy+NDOKu3q1rbaKrL8xJEGeA8ufLlFJNgYFa6/9JZ3U9V5QLh7dB/I/G63QimTaKr5u1YpG3F3m6jGGtBzOt6wz8m8oEO+YqLdOs2p3E+z/EkpxVwKBAL54eHkiLJo7sSDzNtvh0tsWn8/rGKAA8nB3o29aD/u086B/gQcumNWdkcWp2AR9uiWPpX8dwsLPhkRsDeHCAPy6O8shqbaC01pfeQKm9WuvQ85bt0Vp3tWRgV6NHjx56165d1g6j7tIa0qL/TghHtkNpEdg1JNu3H4tcm/J1xn6yS/IY0mYID4U8RIBrgLWjrjW01vwck8pbm6KJTckhpFVTnhseSC9/90q3P5mZz+/xp9gWl8a2+FOk5xQC4O/ZiP4BRrLo3dadxla4GGcXFDN3ayJzfztEcWkZ48NaM3NQgEzKUwMppXZrrXtUus6MBBGhtQ4+b1mk1rpLFcZYJSRBWEBBJiT+YkoKWyDruLHcsyMEDCLHtx+L8w7xVfRisouyGdx6MNNCptHBrYNVw65t9h7L4M2NUew4dBpfdyf+OSyQ4Z2bm91pq7UmJiWbbXHG3cWOxNPkF5dia6MI8WlC/3ae9A/woGvrptjbWu5BgMKSUhb/eZSPf47ndG4RI4Nb8OSQDvh6NLLYOcW1udYEMR/IAD4BNDATcNVaT6zaMK+dJIgqoDUkR0DcD0ZCOLYDdCk0aAz+1xtPG7UdRF4jd76J/oYFBxaQWZjJwFYDeTjkYTq6d7T2J6hVDqfn8q6pbd69kQOPDm7H+LDW13wRLywpZc/RDLbFpfNbfDqRSRmUaWjkYEsvf3fjDqOdB+28nKvkyaGyMs3afcd5b3MsSWfy6RfgzjPDOtLFp8k1H1tY1rUmiEbAi8BgQAGbgX9rrXOrOtBrJQniGhXnw4YnYO9i432LkL/HJfj0BFt78orzWBazjC/3f8mZwjMM8BnAwyEP08mjk3Vjr2XScwr5aEsci3ccxd7WhgcH+DNlgD/ODSxT/SYzr5g/Eo27i21x6Rw+ZTxz0qxxA/qZmqP6B3hc8WxrWmt+iU3jne9iiDqZRaeWjXlmeCDXtZMxLLXFNSWI2kQSxDU4cxiW3Q3JkXDdk9BrKjh7la/OL8lnecxy5u+fz+mC0/Rr2Y+HQx8m2DP44scUF8grKmHeb4f4/NeE8nISjw1qV+3TYB47ncfvps7u3+PTOZNXDED7Zs70D/DkunYehPm50egSCWvP0TO8/V00fyaeprWbE08O7cDILi1q/BNV4lzXegfhCfwT6ASU/yvWWt9YlUFWBUkQVynuB1j1AKDhtrnQfmj5qsLSQlbGrmRe5DzS89Pp3aI300OnE+oVarVwa6OS0jKW70rigx9jScsuZGinZjw1NJAAL+uXkygr0xw8mVV+d/HX4dMUlZRhb6vo2tq1vDkq2LsJdrY2JKTlMOv7GDbtT8bD2YFHBrVjXM/WONjJIMfa6FoTxGZgGfAkMA24D0jTWj9txomHAbMBW2Ce1vqt89a7AvOBtkABcL/Wen+F9bbALuC41nrk5c4nCeIKlZXB1nfhlzehWWe4cyG4GY+jFpUWsSpuFfMi5pGan0pY8zAeDn2Y7s26Wzno2kVrzeaDKbzzXTQJabl0b+PKs8MD6eHrZu3QLqqguJRdh8/wW3wav8enc+BEFlqDi6MdnVo2ZufhMzjaGc1iD1xnuWYxUT0ulSDM+Zt111p/oZR6VGv9K/CrUupXM05qi9GxfROQBOxUSq3TWh+ssNlzwF6t9WilVKBp+0EV1j8KRAGNzYhTXIn8M7B6KsR9D8HjYOQH4OBEcWkxa+LXMCdiDil5KXTz6sab171JWAsZE3mldh85zZsbo9l15Az+no34/J7uDAlqVuPLSTja2xpjKtoZ07Sezi1ie4JxdxF+9Az39G7DjBsD8HBuYOVIhaWZkyCKTX+eVEqNAE4APmbsFwbEa60TAZRSS4FbgYoJIgh4E0BrHa2U8lVKNdNapyilfIARwOvA42Z9GmGe5EijvyHzONw8C3o+QBmajYnr+WTPJyTlJBHiGcJr/V6jd4veNf6CVtMkpOXwznfRfH8gBU+XBrwxugtje/hgZ8HHSy3JrZEDI4NbMjK4pbVDEdXMnATxb6VUE+AJ4COMb/P/Z8Z+3sCxCu+TgF7nbbMPuA3YppQKA9pgJJ8U4D8YfR8uZpxLmGvfMvj2UaMG0qSNaJ+e/Hb8N2aHzyb2TCyBboF8OuhT+nv3l8RwhVKzCvjPljiW7TyGo50Nj9/Ungeu88PJQZpgRO10yX+5pmaidlrr9UAmcMMVHLuyq8v5HR5vAbOVUnuBSGAPUKKUGgmkaq13K6UGXibGKcAUgNatW19BePVMSRF8/xzsnAu+18GY+YTnJjH7u4mEp4bTyqUV7wx4h6G+Q7FRtfObrrXkFJYwZ6sx0U1xaRl392rNzEHtpAlG1HqXTBBa61Kl1Cjgg6s4dhLQqsJ7H4zmqYrHz8KYYwJlfF09ZHqNA0YppW7GeHKqsVJqkdb67kpinAPMAaOT+irirPuyTsDyeyFpJ/SdSUy3CXy441W2Jm3Fs6EnL/Z+kdHtRmNvI/VxrkRx6d8T3aTnFDGiSwueGiqjhkXdYc6973al1McYTzKVD47TWodfZr+dQDullB9wHOOiP6HiBqbCf3la6yLgAWCrKWk8a3phuoN4srLkIMxw6DdYOQmK8zk26gM+yY1l44ZxODs481i3x5jQcQIN7WpOcbfaQGvNxshk3v0+msOn8ujl58a8+zoS2qqptUMTokqZkyD6mv78V4VlGrjkOAitdYlSagbwPcZjrvO11geUUtNM6z8DOgILlVKlGJ3Xk68wfnExWsMfH8MPL5Pm7sfnPf7Bqv0fYWdjx+Quk5nYaSJNGkgZhCu1I/EUb26KZu+xDNo3c2b+xB7c0MFL+mtEnSQjqeuiwmxYO4Os6LV86d+dxWRRXFbC7e1vZ2rwVDydpAzClYpNyebtTdFsiU6leWNHHr+pPbd398FWRg2LWu6axkEopV6qbLnW+l+VLRdWlhZL/rK7WFKczBd+AWSVpjLcbzgzQmfQurF04l+p1KwC3tscy4rdx2jkYMc/h3VgUl8/GjrIRDei7jOnialiUT5HYCTG4DVRwxTvX83/fnySzxo3JNW2Kde1COORbo8Q6BZo7dBqpe3x6cxYsofsgmIm9vVjxo0BuDVysHZYQlSbyyYIrfV7Fd8rpWYB6ywWkbhiZSVFbN4wlY/S/uCoayO6ugXxTtg/pSzGVdJaM/e3RN7aFI2/pzPLp/apETWThKhuVzOCxwmQ+SNrAK01vyds5MNtLxGlimjn6MrH/V9jQJtB0ml6lXILS/jnygg2RJ5keOfmvHtHiNQaEvWWOX0Qkfw9wM0W8OTcJ5qEFexN3cvsP/7NrowYvEtLedP/Hwy//jVsbaRt/GolpuUw9evdJKTl8MzwQKYO8JdEK+o1c74aVayiWgKkaK1LLBSPuIy4M3F8GP4hvyT9gntpKc8V2jJm1NfYe0tz0rXYfCCZJ5bvw85W8fXkXvQL8LB2SEJYnTkJogVwQGudDaCUclZKddJa77BsaKKi4znH+XTvp3yb8C2NsGHm6Qzu9uiJ07h54FRzS0fXdKVlmv/8GMtHP8XTxbsJ/727Gz6uTtYOS4gawZwE8V+gW4X3eZUsExaSnp/O3Ii5LI9dji02TCy25/4TiTS97p9w/dNgI3WTrlZGXhGPLN3L1tg0xvbw4V+3dsbRXprohDjLnAShdIXRdFrrMqWU9NpZWHZRNl8d+IqFBxdSVFrEaK+eTIv4gWZlZTBu6Tmzvokrd+BEJtMW7SY5s4A3RndhfFgr6W8Q4jzmXOgTlVKPYNw1ADwMJFoupPqtsLSQpdFLmRc5j4zCDIa2GcKM4gb4/v6pada3r8HNz9ph1mpr9iTxzKpIXJ0cWD61D11bu1o7JCFqJHMSxDTgQ+AFjKeZtmAqry2q1tr4tXy05yNS8lLo17Ifj3SaSNDP70Hc5nNmfRNXp6ikjDc2RrFg+2F6+bnx8YRueLpISW4hLsacgXKpGJVYhQVtP7GdF35/gS4eXXij/xuE4QjL7zFmfRvxHvSYDNIEctVSswp4eHE4u46c4YH+fjwzPLDWzvAmRHW57P8QpdRXprLcZ9+7KqXmWzSqemhOxBy8nLxYMGwBYamJ8MVNUFIIkzZCzwckOVyDXYdPM+KjbRw4kcWH47vywsggSQ5CmMGcJqZgrXXG2Tda6zNKqa6WC6n+CU8JZ3fKbv7Z/QkcvnsWds4rn/UNZy9rh1draa1Z+McRXlt/EB/Xhnw9OYzA5o2tHZYQtYY5CcJGKeWqtT4DoJRyM3M/YaY5kXNwa9CU2//8GpJ2Qd+ZMOgVsJVf89XKLyrl+TWRrN5znEGBXrx/ZyhNGsqMeUJcCXOuQO9hzCq30vT+DuANy4VUvxw4dYDfj//Oo2VNcDqxD8Z8CZ1vs3ZYtdqx03lM/Xo3UclZ/N/g9sy8MQAbmbdBiCtmTif1QqXULowZ5BRwm9b6oMUjqyfmRczFBRvuPLofbpsnyeEa/RqbxiNL9qC1Zv59PbkhUJrohLhaZrVhmBLCQaVUW2C8Umq51rqzZUOr+xLOxPPj0S1MOZOJy5DXocsYa4dUa5WVaT79JZ73foilQzMXPr+nO23cG1k7LCFqNXOqubYA7gQmAMHAm8B4C8dVL8z76QkalpVxd4ex0Ge6tcOptbIKinli+T5+OJjCraEteeu2YJnxTYgqcNEEoZR6ECMR+ADLgQeAtVrrV6sptjrt2I5P2JSdwF0NWuI69B1rh1NrxaVkM/Xr3Rw9ncfLtwQxsa+vlMwQoopc6g7iE+APYILWeheAUkpfYnthrrgf+WLne9i4NOK+kQuk4N5V2hBxkqdW7sPJwY5vHuxNmJ9UtRWiKl0qQbTEeGLpfaVUM4y7CHlO8Fod303yqomsbe7KbW3/gVdjb2tHVOuUlJbx7vcxfL41kW6tm/Lfu7vTrLGjtcMSos656FdXrXW61vq/WusBwCAgE0hVSkUppeQx16txKgEWj+UrV1e0jQ2TQqZaO6Ja51ROIffO/4vPtyZyT+82LJ3SR5KDEBZi7lNMScAsYJZSqgNSm+nK5aTCots5pTQrnRwY4TcMHxcfa0dVq+w7lsFDi3aTnlvEu2OCuaNHK2uHJESddsWN31rrGOmovkKFObD4DshOZlHPsRSWFTO5y2RrR1WrLNt5lDs++wOlFKsf6ivJQYhqILUcLK20GJbfC8mRZI35gqX73mFwm8H4N/G3dmS1QmFJKa+sO8iSv45yXTsPPhzXFddGDtYOS4h6QRKEJWkN62ZCwhYY9RFLSlLJKc5hSrBMp2GOExn5PLQ4nH3HMnh4YFueGNIBWymZIUS1MWeg3Bat9aDLLROV2PIq7FsCNzxPXpcxLFo1lAE+Awh0C7R2ZDVWWZnm4Mkstiek8/mviRSWlPHZ3d0Z1rm5tUMTot651EA5R8AJ8FBKuWLUYQJojPEI7GUppYYBswFbYJ7W+q3z1rsC84G2QAFwv9Z6v1KqFbAQaA6UAXO01rOv5INZ3Y7PYdsH0H0SDHiKFQcXklGYwYNdHrR2ZDWK1pqEtBy2J5xie/wp/jx0ioy8YgCCfZrw/thQArycrRylEPXTpe4gpgKPYSSD3fydILIwBtFdklLK1rTdTUASsFMpte68Qn/PAXu11qOVUoGm7QcBJcATWutwpZQLsFsp9UOtKRJ4YA1seho6jIAR71FYVsRXB74irHkYoV6h1o7O6o6dzmN7QrqRFBJOkZZdCIB304YMCWpG37Ye9GnrLo+vCmFlF00Qpm/ss5VSM7XWH13FscOAeK11IoBSailwK1DxIh+EUdsJrXW0UspXKdVMa30SOGlanq2UigK8z9u3Zjq8DVZPgVZhMOYLsLFlbcwq0vLTeOO6+jl8JCWrgD8STpUnhaQz+QB4ujSgb1t308uDVm4y37YQNYk5ndTJSikX04X6BaAb8G+tdfhl9vMGjlV4nwT0Om+bfcBtwDalVBjQBqP2U8rZDZRSvkBXYIcZsVpXygFYMgFc/WD8UrBvSHFZMfP3zyfYI5hezc//+HXTmdwi/kw8ZbpDSCchLReAJg3t6ePvzpQB/vRt605bT2epmyREDWZOgnhRa71CKdUfGIoxYO6/XHixP19l//PPr+X0FsZdyl4gEtiD0bxkHEApZ2AV8JjWOqvSkyg1BZgC0Lp168t+GIvJTIJFY8DBCe5eBU5GXaBNhzZxPOc4z4Q9U2cvhtkFxew8fJrt8UZSiErOQmto5GBLmJ8b43q2pk9bd4JaNJaJe4SoRcxJEKWmP0cA/9Var1VKvWLGfklAxdFMPsCJihuYLvqTAJRx9TxkeqGUssdIDou11qsvdhKt9RxgDkCPHj2sU0ww7zQsuh2KcuD+76Cp8bFLy0qZFzmP9q7tud7nequEZgkFxaXsPnKmvMkoIimT0jKNg50NPdq48sRN7enT1oNgnybY20ohQiFqK3MSxHGl1OfAYOBtpVQDzBuBvRNop5TyA45jlOeYUHEDpVRTIE9rXYRRTnyr1jrLlCy+AKK01u+b/WmsoTgflk6A04lw92po1ql81Y9Hf+RQ5iHeHfBurb57KC4tY9+xjPImo/AjGRSVlmFrowht1ZSHB7alT1t3urV2xdFe5mEQoq4wJ0GMBYYBs7TWGaYJhJ663E5a6xKl1Azge4zHXOdrrQ8opaaZ1n8GdAQWKqVKMTqgz9af6AfcA0Samp8AntNabzT/o1WDslJY9QAc/RPGzAe/68pXaa2ZGzEX38a+3NTmJisGeeVKyzQHT2SV3yHsPHyavKJSlIJOLRszsZ8vfdq609PXDecGMtZSiLrKnDmp85RSqUB/IA6jjyDOnIObLugbz1v2WYWf/wDaVbLfNirvw6g5tIaNT0H0ehj29gVzSf92/DdizsTwr77/wtam9nyrXrU7idc2HCwfi9DOy5k7uvvQp60Hvf3daOokZS6EqC/MGUn9MtAD6AB8iTEnxCKMb/n112+zYNcX0O9R6D3tnFVaa+ZEzKFFoxaMbDvSSgFemaKSMv694SAL/zhCmJ8bd/VqTR9/d7xkLIIQ9ZY57QOjMR4zDQfQWp8wDV6rv/Ysgp/+DcF3wqBXLli9M3kn+9L28Xyv57G3qflzLKVmFzB9cTg7D59hygB//jm0A3bSuSxEvWdOgijSWuuz040qpRpZOKaaLXYzrHsE2t4Ioz6udLrQOZFz8Gjoweh2o60Q4JXZfeQMDy3aTXZBCR+O78qoELOqqAgh6gFzviYuNz3F1FQp9SDwIzDPsmHVUEm7YcV90LwzjF0Idhe2x0ekRbDj5A7uC7qPBrYNrBCkebTWLN5xhHFz/sDR3pbVD/eV5CCEOIc5ndSzlFI3YdRg6gC8pLX+weKR1TTp8fDNHeDsBXethAaVt7LNjZhLkwZNGNthbDUHaL6C4lJeXnuAZbuOMbCDJ7Pv7EoTp5rfFCaEqF7mdFK/rbV+GvihkmX1Q3YKLDI9pXT3aiNJVCLmdAy/JP3Cw6EP42RfM+sKncjI56FFu9mXlMnMGwN4bHB7mWNBCFEpc5qYKnuIf3hVB1JjFWYbdw65aTBhBbi3veim8yLn0ci+ERMCJ1x0G2v6M/EUt3y0jYS0XD6/p7tMwCOEuKRLzQfxEPAw4K+UiqiwygX43dKB1QglRbDsHkjeDxOWgU/3i256OPMw3x/+nkmdJ9GkQZNqDPLytNZ8+fthXt8Yha+7E5/f00PmWBBCXNalmpi+ATZhlON+psLybK31aYtGVROUlcHa6ZD4M9z6KbS79GjoL/Z/gYOtA/cE3VNNAZonv6iUZ1dH8L+9JxgS1Iz3xobg4ij9DUKIy7vUfBCZQCYwvvrCqUF+fBkil8ONL0DXuy656YmcE6xPWM/YDmPxaOhRTQFe3rHTeUz9ejdRyVk8OaQ9Dw8MkGqqQgizSSGdyvz5X9j+IfR8AK578rKbf7n/S1AwqfOkagjOPFtj03hk6R7KyjTzJ/bkhg6Vd6wLIcTFSII43/7V8N2z0PEWGP4OXKYKa1peGqvjVjOq7SiaN2peTUFenNaa//6awKzvY2jfzIXP7+lOG/f6PbZRCHF1JEFUdGgrrJkKrXvDbXPBjCJ7Cw8upESXMLnz5Mtua2k5hSU8tWIfm/Ync0tIS96+vQtODvJXLIS4OnL1OCt5Pyy9C9z8YfwSsG942V0yCjJYFrOMYb7DaN3YirPZAYlpOUz9ejcJaTm8MKIjk/v71eo5KIQQ1icJAiDjqDEjnIOzMV1oQ1ezdlscvZj8knwe6PKAhQO8tB8PpvB/y/Zib2fDosm96BtQczrKhRC1lySI/AwjORTnG9OFNvExa7ecohwWRy3mxlY30s71giktqkVZmWb2ljhmb4mji3cTPrunO95NL3/nI4QQ5pAE0cDFqMzacRQ0CzJ7t2Uxy8guymZK8BQLBndxmfnFPL5sL1uiUxnT3Yd//6OzTPcphKhSkiBsbGH421e0S0FJAQsPLqRvy7508uh0+R2qWGxKNlO/3s2x03m8dmsn7u7dRvobhBBVThLEVVgVt4rTBad5sMuD1X7ujZEneXLFPho1sGPJlN709HWr9hiEEPWDJIgrVFxazJf7v6SbVzd6NO9RbectLdO8+30Mn/2aQLfWTfnv3d1pJtOBCiEsSBLEFfo28VtS8lJ4pe8r1XbOM7lFzFyyh23x6dzVqzUv39IJBzuZElQIYVmSIK5ASVkJX0R+QZB7EP1a9quWc+4/nsm0RbtJzSrknduDGduzVbWcVwghJEFcgc2HN3M0+ygfDPygWjqF1+xJ4plVkbg1cmDFtD6EtGpq8XMKIcRZkiDMVKbLmBs5l7ZN2nJj6xsteq7i0jJe3xDFgu2H6eXnxid3dcPDuebOby2EqJskQZjpl2O/EJ8Rzxv938BGWa79Py27kOnfhPPXodPc38+PZ28OxN5W+huEENVPEoQZtNbMiZiDj7MPw/0sN9vqnqNneGhROBn5RcweF8qtod4WO5cQQlyOfDU1wx8n/uDAqQNM7jIZOxvL5NS9xzK4c86f2NspVj/UT5KDEMLq5A7CDHMi5+Dl5MWotqMscvzMvGJmfBOOp3MD1k7vj1sjB4ucRwghroRF7yCUUsOUUjFKqXil1DOVrHdVSq1RSkUopf5SSnU2d9/qEp4Szu6U3UzqNAkH26q/cGuteWrlPpIzC/h4QldJDkKIGsNiCUIpZQt8AgwHgoDxSqnzq+E9B+zVWgcD9wKzr2DfajEncg5ujm7c3v52ixx/wfbDbD6YwjPDA+na2rwy40IIUR0seQcRBsRrrRO11kXAUuDW87YJArYAaK2jAV+lVDMz97W4A6cO8Pvx37kn6B4a2lV9Ge19xzJ4Y2MUgzs2Y3J/vyo/vhBCXAtLJghv4FiF90mmZRXtA24DUEqFAW0AHzP3tbh5EfNwsXfhzg53VvmxM/OLmbEkHC8XR2bdESzVWIUQNY4lE0RlVzx93vu3AFel1F5gJrAHKDFzX+MkSk1RSu1SSu1KS0u7hnDPlZCRwI9Hf2R8x/G4OLhU2XHB6Hd4ZlUEJzMK+HB8V5o6Sb+DEKLmseRTTElAxcJBPsCJihtorbOASQDK+Ap9yPRyuty+FY4xB5gD0KNHj0qTyNWYFzmPhnYNubvj3VV1yHIL/zjCpv3JPHdzIN3bSL+DEKJmsuQdxE6gnVLKTynlAIwD1lXcQCnV1LQO4AFgqylpXHZfSzqWfYxNhzZxR/s7cHWs2gt4ZFImr2+IYlCgFw/096/SYwshRFWy2B2E1rpEKTUD+B6wBeZrrQ8opaaZ1n8GdAQWKqVKgYPA5Evta6lYzzd//3xslA33dbqvSo+bVVDM9G/C8XB2YNYdIdjYSL+DEKLmsuhAOa31RmDjecs+q/DzH0A7c/etDim5KayNX8vogNF4OXlV2XG11jy7KpLjGfksn9obVxnvIISo4aTUxnkWHFhAmS7j/i73V+lxF/15hA2RJ3lqaAe6t5FpQoUQNZ8kiApOF5xmZexKRviPwNu56p6q3X88k9fWRzGwgydTrpN+ByFE7SAJooKvD35NYWkhD3R5oMqOmW3qd3Br5MD7Y0Ol30EIUWtIsT6TrKIslkYv5aY2N+HXpGpGNWuteXZ1JEln8lk6pbfUWRJC1CpyB2GyJGoJOcU5PBj8YJUd85u/jrI+4iRPDGlPT1/pdxBC1C6SIIC84jwWRS1igM8AAt0Cq+SYB05k8uq3B7m+vSfTBrStkmMKIUR1kgQBrIhdQUZhBg92qZq7h5zCEmZ8swdXJ3veHyvjHYQQtVO974MoLC3kqwNfEdY8jFCv0Gs+ntaa51ZHcuRULkse7I27c4NrD1IIIayg3icIgPs730+Qe9VMN7F05zHW7TvBk0Pa08vfvUqOKYQQ1lDvE0QD2wbcHVQ1BfmiTmbxyroDXNfOg4cHBlTJMYUQwlqkD6KK5BaWMP2bcJo0tOeDO2W8gxCi9qv3dxBVQWvNC//bz+H0XBY/0BsP6XcQQtQBcgdRBZbvOsaaPcd5bHB7+rSVfgchRN0gCeIaRSdn8dLaA/QLcGf6DdLvIISoOyRBXIPcwhKmLw6ncUN7/nNnV2yl30EIUYdIH8RV0lrz4v/2k5iey+LJvfB0kX4HIUTdIncQV2nF7iRW7znOIze2o2+Ah7XDEUKIKicJ4irEpmTz0tr99PF355FBlU6IJ4QQtZ4kiCuUV2T0Ozg3sGP2+FDpdxBC1FnSB3GFXlp7gPi0HL6+vxdeLo7WDkcIISxG7iCuwMrdSazcncTMGwLo3076HYQQdZskCDPFpWTz4v/208vPjUcHt7d2OEIIYXGSIMyQX1TK9G/CcXKw5cPxMt5BCFE/SB+EGV5et5+41By+mhRGs8bS7yCEqB/kDuIy1uxJYvmuJKYPDGBAe09rhyOEENVGEsQlxKfm8Pya/YT5uvHYYBnvIISoXyRBXERBcSkzvgnH0d7od7CzlV+VEKJ+kT6Ii3j12wNEJ2ezYFJPmjeRfgchRP1j0a/FSqlhSqkYpVS8UuqZStY3UUp9q5Tap5Q6oJSaVGHd/5mW7VdKLVFKVdtVeu3e4yz56xgPDWzLwA5e1XVaIYSoUSyWIJRStsAnwHAgCBivlAo6b7PpwEGtdQgwEHhPKeWglPIGHgF6aK07A7bAOEvFWlFCWg7PrY6kRxtXnrhJxjsIIeovS95BhAHxWutErXURsBS49bxtNOCilFKAM3AaKDGtswMaKqXsACfghAVjBYx+h+mLw3Gws+GjCdLvIISo3yx5BfQGjlV4n2RaVtHHQEeMi38k8KjWukxrfRyYBRwFTgKZWuvNFowVgH+tP0h0cjbvjw2lRZOGlj6dEELUaJZMEJUNN9bnvR8K7AVaAqHAx0qpxkopV4y7DT/TukZKqbsrPYlSU5RSu5RSu9LS0q462HX7TvDNjqNMvd6fGwKl30EIISyZIJKAVhXe+3BhM9EkYLU2xAOHgEBgMHBIa52mtS4GVgN9KzuJ1nqO1rqH1rqHp+fVDWQ7lJ7Ls6si6N7GlSeHdLiqYwghRF1jyQSxE2inlPJTSjlgdDKvO2+bo8AgAKVUM6ADkGha3lsp5WTqnxgERFkiyLP9DvZ2Nnw4viv20u8ghBCABcdBaK1LlFIzgO8xnkKar7U+oJSaZlr/GfAasEApFYnRJPW01jodSFdKrQTCMTqt9wBzLBFnmdYEtnDhiSHt8W4q/Q5CCHGW0vr8boHaq0ePHnrXrl3WDkMIIWoNpdRurXWPytZJe4oQQohKSYIQQghRKUkQQgghKiUJQgghRKUkQQghhKiUJAghhBCVkgQhhBCiUpIghBBCVKpODZRTSqUBR65ydw8gvQrDqc3kd3Eu+X2cS34ff6sLv4s2WutKC9nVqQRxLZRSuy42mrC+kd/FueT3cS75ffytrv8upIlJCCFEpSRBCCGEqJQkiL9ZpFpsLSW/i3PJ7+Nc8vv4W53+XUgfhBBCiErJHYQQQohKSYIQQghRqXqfIJRSw5RSMUqpeKXUM9aOx5qUUq2UUj8rpaKUUgeUUo9aOyZrU0rZKqX2KKXWWzsWa1NKNVVKrVRKRZv+jfSxdkzWpJT6P9P/k/1KqSVKKUdrx1TV6nWCUErZAp8Aw4EgYLxSKsi6UVlVCfCE1roj0BuYXs9/HwCPYqH50Guh2cB3WutAIIR6/HtRSnkDjwA9tNadMaZVHmfdqKpevU4QQBgQr7VO1FoXAUuBW60ck9VorU9qrcNNP2djXAC8rRuV9SilfIARwDxrx2JtSqnGwADgCwCtdZHWOsOqQVmfHdBQKWUHOAEnrBxPlavvCcIbOFbhfRL1+IJYkVLKF+gK7LByKNb0H+CfQJmV46gJ/IE04EtTk9s8pVQjawdlLVrr48As4ChwEsjUWm+2blRVr74nCFXJsnr/3K9SyhlYBTymtc6ydjzWoJQaCaRqrXdbO5Yawg7oBvxXa90VyAXqbZ+dUsoVo7XBD2gJNFJK3W3dqKpefU8QSUCrCu99qIO3iVdCKWWPkRwWa61XWzseK+oHjFJKHcZoerxRKbXIuiFZVRKQpLU+e0e5EiNh1FeDgUNa6zStdTGwGuhr5ZiqXH1PEDuBdkopP6WUA0Yn0zorx2Q1SimF0cYcpbV+39rxWJPW+lmttY/W2hfj38VPWus69w3RXFrrZOCYUqqDadEg4KAVQ7K2o0BvpZST6f/NIOpgp72dtQOwJq11iVJqBvA9xlMI87XWB6wcljX1A+4BIpVSe03LntNab7ReSKIGmQksNn2ZSgQmWTkeq9Fa71BKrQTCMZ7+20MdLLshpTaEEEJUqr43MQkhhLgISRBCCCEqJQlCCCFEpSRBCCGEqJQkCCGEEJWSBCFEDaCUGigVY0VNIwlCCCFEpSRBCHEFlFJ3K6X+UkrtVUp9bpovIkcp9Z5SKlwptUUp5WnaNlQp9adSKkIptcZUvwelVIBS6kel1D7TPm1Nh3euMN/CYtMIXSGsRhKEEGZSSnUE7gT6aa1DgVLgLqAREK617gb8Crxs2mUh8LTWOhiIrLB8MfCJ1joEo37PSdPyrsBjGHOT+GOMbBfCaup1qQ0hrtAgoDuw0/TlviGQilEOfJlpm0XAaqVUE6Cp1vpX0/KvgBVKKRfAW2u9BkBrXQBgOt5fWusk0/u9gC+wzeKfSoiLkAQhhPkU8JXW+tlzFir14nnbXap+zaWajQor/FyK/P8UViZNTEKYbwswRinlBaCUclNKtcH4fzTGtM0EYJvWOhM4o5S6zrT8HuBX0/waSUqpf5iO0UAp5VSdH0IIc8k3FCHMpLU+qJR6AdislLIBioHpGJPndFJK7QYyMfopAO4DPjMlgIrVT+8BPldK/ct0jDuq8WMIYTap5irENVJK5Witna0dhxBVTZqYhBBCVEruIIQQQlRK7iCEEEJUShKEEEKISkmCEEIIUSlJEEIIISolCUIIIUSl/h/FJ3o6lWzsQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_relu_results, label='CNN-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_bn_results, label='CNN-ReLU-BN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_skip_results, label='CNN-ReLU-BN-Skip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:47:58.890731Z",
     "start_time": "2021-03-25T03:47:58.888071Z"
    }
   },
   "outputs": [],
   "source": [
    "def infoShareBlock(n_filters):\n",
    "    return nn.Sequential(\n",
    "        nn.Conv2d(n_filters, n_filters, (1,1), padding=0), \n",
    "        nn.BatchNorm2d(n_filters),\n",
    "        nn.LeakyReLU())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:47:58.907825Z",
     "start_time": "2021-03-25T03:47:58.891953Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "def cnnLayer(in_filters, out_filters=None, kernel_size=3):\n",
    "    if out_filters is None:\n",
    "        out_filters = in_filters #This is a common pattern, so lets automate it as a default if not asked\n",
    "    padding=kernel_size//2 #padding to stay the same size\n",
    "    return nn.Sequential( # Combine the layer and activation into a single unit\n",
    "        nn.Conv2d(in_filters, out_filters, kernel_size, padding=padding), \n",
    "        nn.BatchNorm2d(out_filters), #The only change, adding BatchNorm2d after our convolution! \n",
    "        nn.LeakyReLU(leak_rate)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:51:31.207820Z",
     "start_time": "2021-03-25T03:47:58.909719Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "877d0389e9c545a58b0247c8da627c66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "cnn_1x1_model = nn.Sequential(\n",
    "    cnnLayer(C, n_filters), \n",
    "    cnnLayer(n_filters),\n",
    "    infoShareBlock(n_filters), #first info block after 2x cnnLayers\n",
    "    cnnLayer(n_filters),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    cnnLayer(n_filters, 2*n_filters), \n",
    "    cnnLayer(2*n_filters),\n",
    "    infoShareBlock(2*n_filters),\n",
    "    cnnLayer(2*n_filters), \n",
    "    nn.MaxPool2d((2,2)),\n",
    "    cnnLayer(2*n_filters, 4*n_filters), \n",
    "    cnnLayer(4*n_filters),\n",
    "    infoShareBlock(4*n_filters),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D*n_filters//4, classes),\n",
    ")\n",
    "#Now train up this model\n",
    "cnn_1x1_results = train_network(cnn_1x1_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)\n",
    "del cnn_1x1_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:51:31.449414Z",
     "start_time": "2021-03-25T03:51:31.208957Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1BhZ2VzIDIgMCBSIC9UeXBlIC9DYXRhbG9nID4+CmVuZG9iago4IDAgb2JqCjw8IC9FeHRHU3RhdGUgNCAwIFIgL0ZvbnQgMyAwIFIgL1BhdHRlcm4gNSAwIFIKL1Byb2NTZXQgWyAvUERGIC9UZXh0IC9JbWFnZUIgL0ltYWdlQyAvSW1hZ2VJIF0gL1NoYWRpbmcgNiAwIFIKL1hPYmplY3QgNyAwIFIgPj4KZW5kb2JqCjEwIDAgb2JqCjw8IC9Bbm5vdHMgWyBdIC9Db250ZW50cyA5IDAgUgovR3JvdXAgPDwgL0NTIC9EZXZpY2VSR0IgL1MgL1RyYW5zcGFyZW5jeSAvVHlwZSAvR3JvdXAgPj4KL01lZGlhQm94IFsgMCAwIDM5Mi4xMzc1IDI2Mi4xODM3NSBdIC9QYXJlbnQgMiAwIFIgL1Jlc291cmNlcyA4IDAgUgovVHlwZSAvUGFnZSA+PgplbmRvYmoKOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDExIDAgUiA+PgpzdHJlYW0KeJydVk1vGzcQve+v4LE5mOYMhxzO0eqHgaAwENdBz4WqODXstI3Tuv33fVxptaQ+6xgWRL2d5Rs+Pg6H3MNweUXu/tkF94DPiyN37S6/W/3923J1e71wy+fhIuDB0xCNPUVN+PE4/+CMQamjxxrY/vw4DJ8GzI0XrjHt/TCksH4pqk9SB5i1iLcOe5wxThhsZpvebTEwfED+vM7/HiRYgy9YRaUEMuTkY0q5cMvZgOLDhnRYINeXYXHnLn8gR8HdfRjqYjSZSSQEM3tL7u7X4Zvwxt09uO/vhpFsoIgnxkG6lbXoaRpi84U4JrWE6JmHex4OwWcuEqzladEzPKZepChTMkTPPLLDk4vnLKqx42nQ0zwMgUNJ+GdG9MyTe54Yk7dAFHPniAY9zROhMBaDJZWM6JmnTDx9WoF9DJE4OfOspQ5q9OqP35cf+8wms+G7mCIz5CXZ5x47sno8VyqIFPUUQs6qOca1cXwph5lK8ZqimjRMM3aOqR6YlI0swkEbJguHmYgxPfKS3FA14Dkugs6FIikF1jyR8RGyLF5yZist2QyeJcswdzZheArhGzI5TDZuMKNAWEPWgGfJrPjEKauw8cSVj3AJBlpKoJZrBs9xccw+FjJOxrIlO2zcgMJ2QahnJF7HwkjEnsNo4Kehluf6/pfV8xd3tVz+9fmX5b/TRH+6vaIbcayKY6rFwH1euZ/dJ8furSOfauX0hPqQS+Za6us+rf8UAmhIpaBOsbnb3VuiqarwveDgl2owMyiCoTiLHgMetWlqI9g8CFM9TpQzrBVQNRxFJCKRrRqyKXGUMHmkKPVQMFYiAmdAKc21OhWu29yUqjqlGYxdvRbhhYCQBNqSfIaBw3jhzCWn1kgqQcIYn80rDhVVuTKWr9ivurU/De/cq5WlKqeVgLsTq4CcAaYMZBlcp+SkgFkh6Gi1WU+o4xMZKe8IqhCL4BPbUbQEH6EVx15RDhEux2HUXlHslEedS1F6RZE8nAsRc68oV3NLSDn3ijJSw5oh3o6i0XAwNcSvV7SeNt6IONQFqaQAhaV7clpcgq01JpI9cYNqorQjrkAfUxpt0ImLElAz5h27YsqCS2esq624FH3NTnbF5WpLWeOduOQzti7SjrhoHdA2rF3caCupXj1EMmtbZQvbBqkX5EBjdqjfwkz7/drT4X4Nsf+z3Wsi5wmOz3p5Fdf93tvaVOLzMi5pajF1vgssj3c8WjG1DVsDY/9KLccaSmxxKrQNjqjrgkPfgTJFLocGRtGbkmxReJxGhpYM6Dq2SWyLLec1bDG0v8nD15S1QYHRDssM6TzdhM2ZPzbgdo0NxazGAT2Xte9eTH33eBphqzO3hzt+ewzVgDblTeiusGXaA+POh9HBmy3fNPjdRVmmZGPxKH/1Uvz25ubidvXj++k+PFaO3elyvM2PFbfVxla6D74yT8adSNtGdMr1YnGzn25X0V5R67aZo2VIkvvEt9gr8w6Gxjzvp31B/9CU+rvhP3h87okKZW5kc3RyZWFtCmVuZG9iagoxMSAwIG9iagoxMTMwCmVuZG9iagoxNiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDg4ID4+CnN0cmVhbQp4nDWMuw3AMAhEe6a4Efg4gPeJUpH92xBbLrh70hPnOcDIPg9H6MQtZEPhpnhJOaE+UTRabzq2SHO/vGQzFxX9M9x9he3mgGQ0SeQh0eVy5Vkpej6X2ht+CmVuZHN0cmVhbQplbmRvYmoKMTcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNTkgPj4Kc3RyZWFtCnicPVJJcsMwDLv7FXwCd0nvSacn5//XAvTUlxBjigAIpneLytn4aStZfuTHLjtLIrd8B22T+4qqB6RugswlxyVXiFnK50qQWLSUmVifqQ7KzzWoVfjCT8xMTIckEUvIST2KsH5eB/egfr2k81tk/KNjg9JbkkwFnRrulaOU2LBUnxrkHjTdlFafmXZlByyNN2SlJnX69dPjB0swpS8S0UGIxJ/kcocsCykH8Xau3kB4V7sg1VMrDztEmo+R3lIFqzkzAUtG0w4jYG3WmCBVArxFawi0FuLyhU4rIj45N3QwTVWehciwUuFYe89oDRe6hrzScHqScf/5H7wr3tfvH9rzZmQKZW5kc3RyZWFtCmVuZG9iagoxOCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIzMiA+PgpzdHJlYW0KeJw1UTtyBTEI630KXSAz5m+fZzOvSu7fRrCTZmEBCQnnPdiIxJcY0h3lim9ZnWYZfieLvPhZKZy8F1GBVEVYIe3gWc5qhsFzI1PgciY+y8wn02LHAqqJOM6OnGYwCDGN62g5HWaaBz0h1wcjbuw0y1UMab1bqtf3Wv5TRfnIupvl1imbWqlb9Iw9icvO66kt7QujjuKmINLhY4f3IF/EnMVFJ9LNfjPlsJI0BKcF8CMxlOrZ4TXCxM+MBE/Z0+l9lIbXPmi6vncv6MjNhEzlFspIxZOVxpgxVL8RzST1/T/Qsz5/mjBURwplbmRzdHJlYW0KZW5kb2JqCjE5IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggNTkgPj4Kc3RyZWFtCnicMzU1VzBQsLQAEqamRgrmRpYKKYZcQD6IlctlaGkOZuWAWRbGQAZIGZxhAKTBmnNgenK40gCp4RBaCmVuZHN0cmVhbQplbmRvYmoKMjAgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA3NSA+PgpzdHJlYW0KeJw1jbERwDAIA3umYATLARP2yaXC+7cJ+GikFxzI5ObBnrJ0sE3nB/TnpE3zQlGQqjDQdGZ9cWjkDlYOF15oSq+K6M9B7weZ6heaCmVuZHN0cmVhbQplbmRvYmoKMjEgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMjcgPj4Kc3RyZWFtCnicRZBLjgMhEEP3nMJHoP5wno6y6tx/Oy460WywJSjXM7kDE3vxyEyUbrxkuDvMFZ/hGTBz+EqYbMRM6E5cI5SuFOFUnwiOy9686aCJe0TOo54FWdXODWoBZ5Lmgu2CLueMFTfOya1G5c30o9cQvml3H9d5h/ZI497DVL7OOdXOu4oIaQtZ1bS+kGwUyQ5rPypN287LELHgbBpW6BzeCLtXdydLGH6/8jAlEz8P3fpRfF03o+sUZydXJmvPeehJ6V/1yqMPxXEp6Bex7cwk91+nU5KpU3tn72n9p7jH+w+QblY5CmVuZHN0cmVhbQplbmRvYmoKMjIgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNTMgPj4Kc3RyZWFtCnicNY/LDcQwCETvrmIaWImvIfVklVO2/+uCnUhGPNkzYwgVEDLwYcU0R8iBL4+6afoNzrnoXiRVD1FWEdgZIgnmwDnEJ1KhJJgTWlXnHFYBBRber076yF15BbjtwNWzHU3723eoTRL2EEdiSvkp4HXrR9skKhrmuRbqAbrXYKVoErIWsMc2MNNKqH07sForz3f/e1x/8ng2hgplbmRzdHJlYW0KZW5kb2JqCjIzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzA0ID4+CnN0cmVhbQp4nD2SO5LDMAxDe52CF8iM+JPk82Qnlff+7T4yyVaASYkAKC91mbKmPCBpJgn/0eHhYjvld9iezczAtUQvE8spz6ErxNxF+bKZjbqyOsWqwzCdW/SonIuGTZOa5ypLGbcLnsO1ieeWfcQPNzSoB3WNS8IN3dVoWQrNcHX/O71H2Xc1PBebVOrUF48XURXm+SFPoofpSuJ8PCghXHswRhYS5FPRQI6zXK3yXkL2DrcassJBaknnsyc82HV6Ty5uF80QD2S5VPhOUezt0DO+7EoJPRK24VjufTuasekamzjsfu9G1sqMrmghfshXJ+slYNxTJkUSZE62WG6L1Z7uoSimc4ZzGSDq2YqGUuZiV6t/DDtvLC/ZLMiUzAsyRqdNnjh4yH6NmvR5led4/QFs83M7CmVuZHN0cmVhbQplbmRvYmoKMjQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMzAgPj4Kc3RyZWFtCnicNVFJbsMwDLzrFfOBAOIuv8dBT+3/rx3SCWBgaEuczREbGxF4icHPQeTGW9aMmvibyV3xuzwVHgm3gidRBF6Ge9kJLm8Yl/04zHzwXlo5kxpPMiAX2fTwRMhgl0DowOwa1GGbaSf6hoTPjkg1G1lOX0vQS6sQKE/ZfqcLSrSt6s/tsy607WtPONntqSeVTyCeW7ICl41XTBZjGfRE5S7F9EGqs4WehPKifA6y+aghEl2inIEnBgejQDuw57afiVeFoHV1n7aNoRopHU//NjQ1SSLkEyWc2dK4W/j+nnv9/AOmVFOfCmVuZHN0cmVhbQplbmRvYmoKMjUgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNDUgPj4Kc3RyZWFtCnicRVC7jUMxDOs9BRcIYP0se553SJXbvz1KRnCFIVo/kloSmIjASwyxlG/iR0ZBPQu/F4XiM8TPF4VBzoSkQJz1GRCZeIbaRm7odnDOvMMzjDkCF8VacKbTmfZc2OScBycQzm2U8YxCuklUFXFUn3FM8aqyz43XgaW1bLPTkewhjYRLSSUml35TKv+0KVsq6NpFE7BI5IGTTTThLD9DkmLMoJRR9zC1jvRxspFHddDJ2Zw5LZnZ7qftTHwPWCaZUeUpnecyPiep81xOfe6zHdHkoqVV+5z93pGW8iK126HV6VclUZmN1aeQuDz/jJ/x/gOOoFk+CmVuZHN0cmVhbQplbmRvYmoKMjYgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzOTIgPj4Kc3RyZWFtCnicPVJLbgUxCNvPKbhApfBNcp6p3u7df1ubzFSqCi8DtjGUlwypJT/qkogzTH71cl3iUfK9bGpn5iHuLjam+FhyX7qG2HLRmmKxTxzJL8i0VFihVt2jQ/GFKBMPAC3ggQXhvhz/8ReowdewhXLDe2QCYErUbkDGQ9EZSFlBEWH7kRXopFCvbOHvKCBX1KyFoXRiiA2WACm+qw2JmKjZoIeElZKqHdLxjKTwW8FdiWFQW1vbBHhm0BDZ3pGNETPt0RlxWRFrPz3po1EytVEZD01nfPHdMlLz0RXopNLI3cpDZ89CJ2Ak5kmY53Aj4Z7bQQsx9HGvlk9s95gpVpHwBTvKAQO9/d6Sjc974CyMXNvsTCfw0WmnHBOtvh5i/YM/bEubXMcrh0UUqLwoCH7XQRNxfFjF92SjRHe0AdYjE9VoJRAMEsLO7TDyeMZ52d4VtOb0RGijRB7UjhE9KLLF5ZwVsKf8rM2xHJ4PJntvtI+UzMyohBXUdnqots9jHdR3nvv6/AEuAKEZCmVuZHN0cmVhbQplbmRvYmoKMjcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA5MCA+PgpzdHJlYW0KeJxNjUESwCAIA++8Ik9QRND/dHrS/1+r1A69wE4CiRZFgvQ1aksw7rgyFWtQKZiUl8BVMFwL2u6iyv4ySUydhtN7twODsvFxg9JJ+/ZxegCr/XoG3Q/SHCJYCmVuZHN0cmVhbQplbmRvYmoKMjggMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNjMgPj4Kc3RyZWFtCnicRZC5dQQxDENzVYESeIA66hk/R7P9pwtpvN5A+niEeIg9CcNyXcWF0Q0/3rbMNLyOMtyN9WXG+KixQE7QBxgiE1ejSfXtijNU6eHVYq6jolwvOiISzJLjq0AjfDqyx0Nb25l+Oq9/7CHvE/8qKuduYQEuqu5A+VIf8dSP2VHqmqGPKitrHmravwi7IpS2fVxOZZy6ewe0wmcrV/t9A6jnOoAKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDUyID4+CnN0cmVhbQp4nDM2M1QwUDCxVDAyNlEwNjQCYhOFFEMuoAiIlcsFE8sBs0CqcrigynNgqnK40gDpCQ3ACmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMjAgPj4Kc3RyZWFtCnicNVG7ccUwDOs1BRfwnfiVNI9zr8rbvw1AOxVhGgRAqrxkSrlc6pJVssLkR4fqFE35PmCm/A71kOPoHtkhulPWlnsYCMvEPKWOWE2We7gFgS8MTYm5hfP3COgrBqMwE4G6xd8/QLMkMGlw8FOQa61aYokOPCwWWLMrzK0aKVTIVXw7NrkHBXJxs9CnHJoUt9yC8GWIZEdqsa/LZSnyu/UJGIQV5ohPFImF54EOZiLxJwNie/bZYldXL6oRGdZJhwdSBNJsbhIwNEWy6oMb2FfHNT9PR9nByUG/isH4NjiZL0l5XwWhEI8X/g7P2cixkkMkFPJ9tcCII2yAEaFP7SMQZSA0RffumVI+JlWK7wBGIRx9qlcyvBeR2WqGzf8ZXdkqCgZVWR+fRnAmg0k482SjCtNStdO/+9zj8wdjY3qACmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMTQgPj4Kc3RyZWFtCnicPVC7EUMxCOs9BQvkznztN8/Lpcv+bSScpEI2QhKUmkzJlIc6ypKsKU8dPktih7yH5W5kNiUqRS+TsCX30ArxfYnmFPfd1ZazQzSXaDl+CzMqqhsd00s2mnAqE7qg3MMz+g1tdANWhx6xWyDQpGDXtiByxw8YDMGZE4siDEpNBv+tcvdS3O89HG+iiJR08K755fTLzy28Tj2ORLq9+YprcaY6CkRwRmryinRhxbLIQ6TVBDU9A2u1AK7eevk3aEd0GYDsE4njNKUcQ//WuMfrA4eKUvQKZW5kc3RyZWFtCmVuZG9iagozMiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDgwID4+CnN0cmVhbQp4nEWMuw3AMAhEe6ZgBH4mZp8olbN/GyBK3HBPunu4OhIyU95hhocEngwshlPxBpmjYDW4RlKNneyjsG5fdYHmelOr9fcHKk92dnE9zcsZ9AplbmRzdHJlYW0KZW5kb2JqCjMzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjM2ID4+CnN0cmVhbQp4nE1QS25EIQzbc4pc4EkkIQHOQ9VV5/7bscNU7SqGGH9ID+myVR7rU2J1iezypU2XyjJ5FajlT9v/UQwCbv/QyEG0t4ydYuYS1sXCJDzlNCMbJ9csH487TxtmhcbEjeOdLhlgnxYBNVuVzYE5bTo3QLqQGreqs95kUAwi6kLNB5MunKfRl4g5nqhgSncmtZAbXD7VoQNxWr0KuWOLk2/EHFmhwGHQTHHWXwHWqMmyWcggSYYhzn2je5QKjajKeSsVwg+ToRH1htWgBpW5haKp5ZL8HdoCMAW2jHXpDEqBqgDB3yqnfb8BJI1dUwplbmRzdHJlYW0KZW5kb2JqCjM0IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggNDkgPj4Kc3RyZWFtCnicMza0UDBQMDQwB5JGhkCWkYlCiiEXSADEzOWCCeaAWQZAGqI4B64mhysNAMboDSYKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE1NyA+PgpzdHJlYW0KeJxFkLkRQzEIRHNVQQkSsAjqscfRd/+pF/lKtG8ALYevJVOqHyciptzXaPQweQ6fTSVWLNgmtpMachsWQUoxmHhOMaujt6GZh9TruKiquHVmldNpy8rFf/NoVzOTPcI16ifwTej4nzy0qehboK8LlH1AtTidSVAxfa9igaOcdn8inBjgPhlHmSkjcWJuCuz3GQBmvle4xuMF3QE3eQplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzMyID4+CnN0cmVhbQp4nC1SOY4kMQzL/Qp+YADr8vGeHkzU+/90SVUFBapsyzzkcsNEJX4skNtRa+LXRmagwvCvq8yF70jbyDqIa8hFXMmWwmdELOQxxDzEgu/b+Bke+azMybMHxi/Z9xlW7KkJy0LGizO0wyqOwyrIsWDrIqp7eFOkw6kk2OOL/z7FcxeCFr4jaMAv+eerI3i+pEXaPWbbtFsPlmlHlRSWg+1pzsvkS+ssV8fj+SDZ3hU7QmpXgKIwd8Z5Lo4ybWVEa2Fng6TGxfbm2I+lBF3oxmWkOAL5mSrCA0qazGyiIP7I6SGnMhCmrulKJ7dRFXfqyVyzubydSTJb90WKzRTO68KZ9XeYMqvNO3mWE6VORfgZe7YEDZ3j6tlrmYVGtznBKyV8NnZ6cvK9mlkPyalISBXTugpOo8gUS9iW+JqKmtLUy/Dfl/cZf/8BM+J8AQplbmRzdHJlYW0KZW5kb2JqCjM3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzE3ID4+CnN0cmVhbQp4nDVSS3JDMQjbv1Nwgc6Yv32edLJq7r+thCcrsC1AQi4vWdJLftQl26XD5Fcf9yWxQj6P7ZrMUsX3FrMUzy2vR88Rty0KBFETPfgyJxUi1M/U6Dp4YZc+A68QTikWeAeTAAav4V94lE6DwDsbMt4Rk5EaECTBmkuLTUiUPUn8K+X1pJU0dH4mK3P5e3KpFGqjyQgVIFi52AekKykeJBM9iUiycr03VojekFeSx2clJhkQ3SaxTbTA49yVtISZmEIF5liA1XSzuvocTFjjsITxKmEW1YNNnjWphGa0jmNkw3j3wkyJhYbDElCbfZUJqpeP09wJI6ZHTXbtwrJbNu8hRKP5MyyUwccoJAGHTmMkCtKwgBGBOb2wir3mCzkWwIhlnZosDG1oJbt6joXA0JyzpWHG157X8/4HRVt7owplbmRzdHJlYW0KZW5kb2JqCjM4IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTcgPj4Kc3RyZWFtCnicMza0UDCAwxRDLgAalALsCmVuZHN0cmVhbQplbmRvYmoKMzkgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxMzEgPj4Kc3RyZWFtCnicRY/LDQQhDEPvVOES8hk+qYfVntj+r+swmkFC+EEiO/EwCKzz8jbQxfDRosM3/jbVq2OVLB+6elJWD+mQh7zyFVBpMFHEhVlMHUNhzpjKyJYytxvhtk2DrGyVVK2DdjwGD7anZasIfqltYeos8QzCVV64xw0/kEutd71Vvn9CUzCXCmVuZHN0cmVhbQplbmRvYmoKNDAgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNDggPj4Kc3RyZWFtCnicLVE5kgNBCMvnFXpCc9PvscuR9//pCsoBg4ZDIDotcVDGTxCWK97yyFW04e+ZGMF3waHfynUbFjkQFUjSGFRNqF28Hr0HdhxmAvOkNSyDGesDP2MKN3pxeEzG2e11GTUEe9drT2ZQMisXccnEBVN12MiZw0+mjAvtXM8NyLkR1mUYpJuVxoyEI00hUkih6iapM0GQBKOrUaONHMV+6csjnWFVI2oM+1xL29dzE84aNDsWqzw5pUdXnMvJxQsrB/28zcBFVBqrPBAScL/bQ/2c7OQ33tK5s8X0+F5zsrwwFVjx5rUbkE21+Dcv4vg94+v5/AOopVsWCmVuZHN0cmVhbQplbmRvYmoKNDEgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNzEgPj4Kc3RyZWFtCnicTZBNDkIhEIP3nKIXMKHzA4/zaFzp/bd28PnigvRLIUOnwwMdR+JGR4bO6HiwyTEOvAsyJl6N85+M6ySOCeoVbcG6tDvuzSwxJywTI2BrlNybRxT44ZgLQYLs8sMXGESka5hvNZ91k35+u9Nd1KV199MjCpzIjlAMG3AF2NM9DtwSzu+aJr9UKRmbOJQPVBeRstkJhailYpdTVWiM4lY974te7fkBwfY7+wplbmRzdHJlYW0KZW5kb2JqCjQyIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggODcgPj4Kc3RyZWFtCnicNU25EcAwCOuZghHMo9jsk0vl7N8G7LhBOn0glBtr5AGC4Z1vIfimLxmEdQhPKrslOmyhhrMKkonhVzZ4Va6K9rWSiexspjHYoGX60c63Sc8Hpd4bmAplbmRzdHJlYW0KZW5kb2JqCjQzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTM4ID4+CnN0cmVhbQp4nD2PQQ4DMQgD73mFPxApdkJY3rNVT9v/X0ua3V7QCIwxFkJDb6hqDpuCDceLpUuo1vApiolKDsiZYA6lpNIdZ5F6YjgY3B60G87isen6EbuSVn3Q5ka6JWiCR+xTadyWcRPEAzUF6inqXKO8ELmfqVfYNJLdtLKSazim373nqev/01XeX1/fLowKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIxMCA+PgpzdHJlYW0KeJw1UMsNQzEIu2cKFqgUAoFknla9df9rbdA7YRH/QljIlAh5qcnOKelLPjpMD7Yuv7EiC611JezKmiCeK++hmbKx0djiYHAaJl6AFjdg6GmNGjV04YKmLpVCgcUl8Jl8dXvovk8ZeGoZcnYEEUPJYAlquhZNWLQ8n5BOAeL/fsPuLeShkvPKnhv5G5zt8DuzbuEnanYi0XIVMtSzNMcYCBNFHjx5RaZw4rPWd9U0EtRmC06WAa5OP4wOAGAiXlmA7K5EOUvSjqWfb7zH9w9AAFO0CmVuZHN0cmVhbQplbmRvYmoKMTQgMCBvYmoKPDwgL0Jhc2VGb250IC9EZWphVnVTYW5zIC9DaGFyUHJvY3MgMTUgMCBSCi9FbmNvZGluZyA8PAovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNDUgL2h5cGhlbiAvcGVyaW9kIDQ4IC96ZXJvIC9vbmUgL3R3byA1MiAvZm91ciA1NCAvc2l4IDU2Ci9laWdodCAvbmluZSA2NSAvQSAvQiAvQyA3NiAvTCA3OCAvTiA4MiAvUiA4NSAvVSA5NyAvYSA5OSAvYyAxMDEgL2UgMTA0IC9oCjExMSAvbyAvcCAxMTQgL3IgL3MgL3QgL3UgMTIwIC94IC95IF0KL1R5cGUgL0VuY29kaW5nID4+Ci9GaXJzdENoYXIgMCAvRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250RGVzY3JpcHRvciAxMyAwIFIKL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0gL0xhc3RDaGFyIDI1NSAvTmFtZSAvRGVqYVZ1U2FucwovU3VidHlwZSAvVHlwZTMgL1R5cGUgL0ZvbnQgL1dpZHRocyAxMiAwIFIgPj4KZW5kb2JqCjEzIDAgb2JqCjw8IC9Bc2NlbnQgOTI5IC9DYXBIZWlnaHQgMCAvRGVzY2VudCAtMjM2IC9GbGFncyAzMgovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TmFtZSAvRGVqYVZ1U2FucyAvSXRhbGljQW5nbGUgMAovTWF4V2lkdGggMTM0MiAvU3RlbVYgMCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL1hIZWlnaHQgMCA+PgplbmRvYmoKMTIgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTUgMCBvYmoKPDwgL0EgMTYgMCBSIC9CIDE3IDAgUiAvQyAxOCAwIFIgL0wgMTkgMCBSIC9OIDIwIDAgUiAvUiAyMSAwIFIgL1UgMjIgMCBSCi9hIDIzIDAgUiAvYyAyNCAwIFIgL2UgMjUgMCBSIC9laWdodCAyNiAwIFIgL2ZvdXIgMjcgMCBSIC9oIDI4IDAgUgovaHlwaGVuIDI5IDAgUiAvbmluZSAzMCAwIFIgL28gMzEgMCBSIC9vbmUgMzIgMCBSIC9wIDMzIDAgUiAvcGVyaW9kIDM0IDAgUgovciAzNSAwIFIgL3MgMzYgMCBSIC9zaXggMzcgMCBSIC9zcGFjZSAzOCAwIFIgL3QgMzkgMCBSIC90d28gNDAgMCBSCi91IDQxIDAgUiAveCA0MiAwIFIgL3kgNDMgMCBSIC96ZXJvIDQ0IDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTQgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMSA8PCAvQ0EgMCAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+Ci9BMiA8PCAvQ0EgMSAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+Ci9BMyA8PCAvQ0EgMC44IC9UeXBlIC9FeHRHU3RhdGUgL2NhIDAuOCA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+PgplbmRvYmoKNyAwIG9iago8PCA+PgplbmRvYmoKMiAwIG9iago8PCAvQ291bnQgMSAvS2lkcyBbIDEwIDAgUiBdIC9UeXBlIC9QYWdlcyA+PgplbmRvYmoKNDUgMCBvYmoKPDwgL0NyZWF0aW9uRGF0ZSAoRDoyMDIxMDMyNDIzNTEzMS0wNCcwMCcpCi9DcmVhdG9yIChNYXRwbG90bGliIHYzLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZykKL1Byb2R1Y2VyIChNYXRwbG90bGliIHBkZiBiYWNrZW5kIHYzLjMuMikgPj4KZW5kb2JqCnhyZWYKMCA0NgowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDAxMTMxMyAwMDAwMCBuIAowMDAwMDExMDc2IDAwMDAwIG4gCjAwMDAwMTExMDggMDAwMDAgbiAKMDAwMDAxMTI1MCAwMDAwMCBuIAowMDAwMDExMjcxIDAwMDAwIG4gCjAwMDAwMTEyOTIgMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzk2IDAwMDAwIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwMTYwMSAwMDAwMCBuIAowMDAwMDA5Njc4IDAwMDAwIG4gCjAwMDAwMDk0NzggMDAwMDAgbiAKMDAwMDAwOTAwNiAwMDAwMCBuIAowMDAwMDEwNzMxIDAwMDAwIG4gCjAwMDAwMDE2MjIgMDAwMDAgbiAKMDAwMDAwMTc4MiAwMDAwMCBuIAowMDAwMDAyMTE0IDAwMDAwIG4gCjAwMDAwMDI0MTkgMDAwMDAgbiAKMDAwMDAwMjU1MCAwMDAwMCBuIAowMDAwMDAyNjk3IDAwMDAwIG4gCjAwMDAwMDI5OTcgMDAwMDAgbiAKMDAwMDAwMzIyMyAwMDAwMCBuIAowMDAwMDAzNjAwIDAwMDAwIG4gCjAwMDAwMDM5MDMgMDAwMDAgbiAKMDAwMDAwNDIyMSAwMDAwMCBuIAowMDAwMDA0Njg2IDAwMDAwIG4gCjAwMDAwMDQ4NDggMDAwMDAgbiAKMDAwMDAwNTA4NCAwMDAwMCBuIAowMDAwMDA1MjA4IDAwMDAwIG4gCjAwMDAwMDU2MDEgMDAwMDAgbiAKMDAwMDAwNTg4OCAwMDAwMCBuIAowMDAwMDA2MDQwIDAwMDAwIG4gCjAwMDAwMDYzNDkgMDAwMDAgbiAKMDAwMDAwNjQ3MCAwMDAwMCBuIAowMDAwMDA2NzAwIDAwMDAwIG4gCjAwMDAwMDcxMDUgMDAwMDAgbiAKMDAwMDAwNzQ5NSAwMDAwMCBuIAowMDAwMDA3NTg0IDAwMDAwIG4gCjAwMDAwMDc3ODggMDAwMDAgbiAKMDAwMDAwODEwOSAwMDAwMCBuIAowMDAwMDA4MzUzIDAwMDAwIG4gCjAwMDAwMDg1MTIgMDAwMDAgbiAKMDAwMDAwODcyMyAwMDAwMCBuIAowMDAwMDExMzczIDAwMDAwIG4gCnRyYWlsZXIKPDwgL0luZm8gNDUgMCBSIC9Sb290IDEgMCBSIC9TaXplIDQ2ID4+CnN0YXJ0eHJlZgoxMTUzMAolJUVPRgo=\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABHs0lEQVR4nO3dd3iUVfbA8e9JIyG0hCS0AKGEEnpVAVFBRAELiAsiFnAVFRXUteGurAUXF13E8rOgIFaQKk1EEVFUlF4DJIQWagokpJc5vz8mYMAAATKZlPN5njxk3npmgDnve+97zxVVxRhjjDmTh7sDMMYYUzJZgjDGGFMgSxDGGGMKZAnCGGNMgSxBGGOMKZCXuwMoSkFBQRoWFubuMIwxptRYu3ZtvKoGF7SuTCWIsLAw1qxZ4+4wjDGm1BCRvWdbZ01MxhhjCmQJwhhjTIEsQRhjjClQmeqDKEh2djaxsbFkZGS4OxRTTHx9fQkNDcXb29vdoRhTqpX5BBEbG0vlypUJCwtDRNwdjnExVSUhIYHY2FgaNGjg7nCMKdXKfBNTRkYG1atXt+RQTogI1atXtztGY4pAmU8QgCWHcsb+vo0pGuUiQRhjTFmkqqw8sJKPNn/kkuNbgigGhw8fZvDgwTRq1IiIiAj69OnDzp07ERHeeuutU9s9/PDDfPzxxwDcc8891KlTh8zMTADi4+M52yjxf//739SpU4e2bdsSERHBl19+ed6YKlWqdNrrPXv20LJly78c97XXXruAd2qMKQ7ZjmwW7FrArQtu5cHvH+SrHV+RmZtZ5OexBOFiqkr//v25+uqr2bVrF9u2beOVV17hyJEjhISEMGnSJLKysgrc19PTkylTphTqPI899hgbNmzg66+/ZsSIEWRnZxfl2zDGlAApWSlM2zqNG2bfwJiVY1BVXu76Mgv7L6SCZ4UiP59LE4SIXC8iO0QkWkSeKWB9gIjMFZFNIvKHiLTMt+4xEdkqIltE5EsR8XVlrK6yfPlyvL29eeCBB04ta9u2LXXr1iU4OJiePXsybdq0AvcdPXo0EydOJCcnp9DnCw8Pp2LFihw7dgyACRMm0KlTJ1q3bs3YsWMv7c0YY9wiLi2OiWsnct2s63htzWvUq1KPd3q+w5yb5nBz45vx9nTNI90ue8xVRDyBd4BeQCywWkTmq+q2fJuNATaoan8RaZa3fU8RqQM8CkSoarqIfAUMBj6+lJheWLCVbQeTL+UQfxFRuwpjb2xx1vVbtmyhQ4cOZ13/zDPPcMMNNzB8+PC/rKtXrx7dunXj008/5cYbbyxUPOvWrSM8PJyQkBCWLl1KVFQUf/zxB6rKTTfdxE8//UT37t0LdSxjjHvFJMUwbes0FuxaQK7mcm29axnWchgtg1qef+ci4MpxEJ2BaFWNARCR6cDNQP4EEQH8B0BVt4tImIjUyBebn4hkAxWBgy6M1W0aNGhA586d+eKLLwpcP2bMGG666Sb69u17zuNMnDiRyZMnExMTw5IlSwBYunQpS5cupV27dgCkpKQQFRVVYII425M/9kSQMcVLVVl/dD1Tt07lx/0/4uvpy4DwAdwdcTd1q9Qt1lhcmSDqAPvzvY4FLjtjm43AAGCliHQG6gOhqrpWRF4D9gHpwFJVXVrQSUTkfuB+cF5xn8u5rvRdpUWLFsyaNeuc24wZM4aBAwcW+MXduHFj2rZty1dffXVq2XPPPceiRYsA2LBhA+Dsg/jHP/7BnDlzuOuuu9i1axeqyrPPPsuIESPOG2f16tVPNUudlJiYaIPNjCkmuY5cftz/I1O2TmFT3CaqVajGg20eZHCzwQT6Bp6xcQ7ERULsGjiwBjJPwN8+KfKYXNkHUdClp57xejwQICIbgEeA9UCOiATgvNtoANQG/EVkaEEnUdUPVLWjqnYMDi6wpLlb9ejRg8zMTCZPnnxq2erVq9m7988Ku82aNSMiIoKFCxcWeIznnnvutKeJxo0bx4YNG04lh/wGDBhAx44dmTZtGr1792bKlCmkpKQAcODAAY4ePVrgOSpVqkStWrVYtmwZ4EwOS5YsoVu3bhf8no0xhZeZm8nMnTO5+eubGf3jaBLSExhz2RiWDlzKQ20fciaH5IOwbT589zxM7Qvj68F73WDhaNi+CHKywOEo8thceQcRC+S/HwrljGYiVU0GhgGIsy1jd95Pb2C3qsblrZsDdAE+c2G8LiEizJ07l9GjRzN+/Hh8fX0JCwvjjTfeOG2755577lRT0JlatGhB+/btWbduXaHO+fzzzzNkyBAiIyOJjIzkiiuuAJxJ4LPPPiMkJIS0tDRCQ0NP7fP444/zySefMHLkSJ544gkAxo4dS6NGjS7iXRtjzicpM4kZO2bweeTnJGYkElE9gglXTeDamlfgdXgL/P4BxK6G2LVwIu+r08MbaraCdndAaCeo0wECG4KLmoJF9cyL+iI6sIgXsBPoCRwAVgNDVHVrvm2qAWmqmiUi9wFXqupdInIZMAXohLOJ6WNgjaq+xTl07NhRz5wwKDIykubNmxfZ+zKlg/29m5LqYMpBPt32KbOjZpOek0634HYMq9SETsePIgfWwdFtoLnOjQPCoE5HCO3o/LNmK/Au2gc6RWStqnYsaJ3L7iBUNUdEHga+BTyBKaq6VUQeyFv/HtAc+EREcnF2Xt+bt+53EZkFrANycDY9feCqWI0xxtW2J25n6ob3+Hb/cgSlD/7cfTSZJru/dm5QoSrUaQ9XPv5nUvAPcmvMLq3mqqqLgcVnLHsv3++/AeFn2XcsYA/uG2NKp+x09OBGfouax9RDP7HKcYKKDgdDT6QwNDmNmkHNoPmAvKaijlC9MXiUrLHLZb7ctzHGuJwqJOxyPlEUu5rs2NUsPRHDx1X82V7BhyCHMtqnDrfV60WVel2hVhvwqejuqM/LEoQxxlwoVTiwDqKWOjuSD6yFjOOkiTCnWiCfVK3CId8AGvqG8GLEnfSNGIKPp4+7o75gliCMMaYwVJ3jDrbNg21fQ9J+EA8Ibk58s+v5wsfBjOObSc5OpX1IG8a0HEb30O54SMlqNroQliCMMeZsHA7nHcK2r50/ybHg6QONesA1Y9hTuyXTds1lfvR8sh3Z9KjXg3ta3EPbkLbujrxIlN7UVoqUtXLfH3/8McHBwbRt25YWLVowcOBA0tLSTu1TsWLF0wbknXkuY0o0hwP2rYJvnoE3WsKU62D1ZKjZisN9J7D4b+/ycsNWDNw3h5u+Gcr86Pnc1Pgm5t8ynzeueaPMJAewBOFyZbXc96BBg9iwYQNbt27Fx8eHGTNmnFoXFBTE66+/7tLzG1OkHA7Y+yssfgomRsCU3uSumcKOGk2ZfuUInuo2lOt8Eum17S2eXvUCC3YtIMA3gIfbPcy3A79l7BVjCasa5u53UeSsicnFzlbue8+ePQQHB9O1a1emTZvGfffd95d9T5b7Lmjd2eQv9x0SEsKECRP46quvyMzMpH///rzwwgtF8r5OysnJITU1lYCAgFPLhg8fzscff8zTTz9NYGDgOfY2xo0cuc47hW3zYNt80lOPsKViJdbXasa6Bs3YmHGYlOxoiI0m2C+YdiHtuLvF3bQLaUeTgCZ4eZT9r8+y/w7z++YZOLy5aI9ZsxXcMP6sq8tque8ZM2awcuVKDh06RJMmTU6Lr1KlSgwfPpxJkyYVeUIy7qWqpbvCryPXeaewbR4J2xewITeZdRX92RASxDb1JQcH5B6hsWdlbmjQh3Yh7WgX0o46leqU7vd9kcpXgiiBSmu570GDBvH222+jqowcOZIJEybwzDN/zgn16KOP0rZt21N1nUzplevI5ffDv7MoZhE/7PsBTw9PavnXoqZ/TWpWrEmtSrVO/VnLvxZBfkEl6+o6Nwfds5K9W75g/b4VrJMsNvj6sieoAhCMj4cPLYOacXdIO9rXaE+b4DZUrVDV3VGXCCXob7EYnONK31XKQrnv/fv3n7pDeOCBB/D1/bMWjIhw44038tZbb52WIKpVq8aQIUP4v//7v/Oe25Q8qkpkYiQLYxayZPcS4tLjqOxdmZ71euLr5cvh1MMcTDnI2iNrOZF14rR9PcSDkIohzqThX4ualWr++bu/88+qFaq69Io8OyudyG0zWL/za9Yd284GbyHR0xOq+FDVsyrtanagf81OtA9pT0T1iFI5RqE4lK8E4QY9evRgzJgxTJ48+VRfwurVq0899QOnl/vu3LnzX47x3HPPnXYHMW7cOMaNG1fg+QYMGMC0adNOlfv+17/+xR133EGlSpU4cOAA3t7ehISE/GW//OW+e/bsearc96hRo6hbt+5ppcVPPml10sqVKwus+vr444/TqVOnC5oy1bhX7IlYFu9ezKKYRcQkxeDt4U330O70a9iPK0OvLHDe49TsVA6nHuZw6mEOpR7iUOqhU6+3Jmzl+33fk+04/aEJPy8/alSscVrSqOlf87Tffb0KX5QuOSuZjYfXsT56AesP/cHm7GNk5iWgun7+dAtsTruGN9C+9uWEVQ0r1WMTipMlCBcrq+W+T/ZBOBwOQkND/5I0wPk0U//+/Zk4cWKh4jbucTzjOEv3LmVRzCLWHXX+G+tQowNjI8bSq36v8za3+Hv706haIxpVK/jfikMdJGYkciT1yKnkcTKRHEk9wsoDK4lPj0fPmC4moELAX5LGyT+rVKhCZEIk64+sZX3sL0SlHUQBT1WaZedyW8U6tAvrQbtWdxJczLOwlSUuK/ftDlbu25xkf+/nlpmbyYr9K1gYs5CfD/xMjiOHRlUb0a9RP/o06EPtSrWLNZ7s3GyOpB057e4jfzI5knqEE9kn/rJfRYfSJjODdtnQPqQ9rVoMomLTPuDtV6zxl2ZuKfdtjClZHOpgzeE1LIxZyHd7vyMlO4Vgv2DuaHYH/Rr1o2lAU7c9qePt6U1o5VBCK+fd1ToczlIWcTsgfgewgxNxkRw+vovDuekc8/QkXH0Ib9gLrxb9nSObi3ieBGMJwpgyb0fiDhbFLGLR7kUcTTuKv7c/19a7ln6N+tGpRic8PTzdF1xuNiTG/JkI4vJ+EqIh+89+OvyDqRzUlMrNBhAe3BSCm0G9y8Hrr30ipuhYgjCmDDqcephFMYtYGLOQ6OPReIkX3ep048lOT3J16NUX1AFcJLLSICEK4nZC3Pa8ZLATEneBI99DDFXrQnBTCLsSgptAUFPn64o24NIdLEEYU0YkZyXz3Z7vWBizkDVHnH1xbYPb8txlz9E7rDcBvgHnOUIRSD8O8Tvz7gS2//n78X1wshNaPCGwgfMuoFlfZwIIbgrVw6GC1e26UNm5DvYmpNI4pHKRH9sShDGlWFZuFj/H/szCmIWsiF1BtiObsCphjGw7kr4N+lLXFU/wqELK0dObhE7+nnLkz+08K0BQuHPqzLZ3OO8IgptBYENrGioiP+44yksLt3EiI4cVT16Dn0/RNhdagjCmlHGog3VH1rEwZiFL9y7lRNYJAn0DGdR0EP0a9iOiekTRdzZnpcHWubDxSzi8CTKS/lznU9l5B9D4Wghq8ucdQbX64M7+jTIsJi6FlxdF8sP2o4RVr8gr/Vvh6+2CsR2qWmZ+OnTooGfatm3bX5YVt0OHDumgQYO0YcOG2rx5c73hhht0x44dCuibb755aruRI0fq1KlTVVX17rvv1tq1a2tGRoaqqsbFxWn9+vULPP7YsWO1du3a2qZNG23evLl+8cUX543J39//tNe7d+/WFi1a/OW4EyZM+Mu+U6dO1aCgIG3Tpo1GRETorbfeqqmpqaf28fPz0yNHjpz1XCdFRkbq5Zdfrj4+PgWepyC9e/fWqlWrat++fc+5XUn4ey9qUYlROnHNRO01s5e2/Lildvqskz7z0zO6MnalZudmu+akBzeqLnxc9ZW6qmOrqL7ZXnXBaNXf3lWN/kE16YCqw+Gac5u/SErP0pcXbtXGYxZpi+eX6PsrojUjO+eSjgms0bN8p9odhItpXrnvu+++m+nTpwPO8hj5y32PGDECH5+/DvU/We77wQcfPO95TpbaiIqKokOHDgwcOBBvb+8ifz8nnazFBDBkyBBmzJjBsGHDgD/Lfb/66qvnPEZgYCBvvvkm8+bNK/R5n3zySdLS0nj//fcvOvbSJDU7lUUxi5i1cxaRiZF4iidX1L6CUe1HcU3da6jo7YJ5jTNTYMtsWPsxHFznbCpqcQu0vxvqd4FyWLTO3XIdyldr9vPatztITMvibx3q8o/eTQmu7NqmOhtv7mJnK/ddt25dgoOD6dmzJ9OmTStw35Plvi+kVEX+ct8AEyZMoFOnTrRu3ZqxY8de2pspwNnKfc+YMYPExMRz7hsSEkKnTp3+kshWr15N69atycjIIDU1lRYtWrBlyxYAevbsSeXKRd8ZV9JEJkTy4m8v0uOrHry06iVyNZenOz3N97d9z7vXvkvfhn2LNjmcnGN5wSh4vSkseBSy0+H6V+GJ7TDgAwjrasnBDX6PSeDGt1by7JzNNAjyZ/7Ibrw6sLXLkwOUsz6IV/94le2J24v0mM0Cm/F056fPut7KfV94ue9OnTpx00038c9//pP09HSGDh36l9nuyqL0nHSW7F7CzJ0z2Ry/mQqeFegd1pu/Nf0brYNau2YQW0YSbJ4Ja6c5+xa8/KDlAOhwD4R2soTgRrHH0vjPN9tZtOkQtav68tbt7ejXulaxDmYsVwmiJLJy3wV7/vnn6dSpE76+vrz55psXdYzSIupYFDN3zmThroWcyD5Bw6oNebrT09zY6EbXlJ1Whdg1ziakrXOcA9JqtII+r0Gr28CvWtGf0xRaWlYO762I4f0VuxCB0deGM6J7oyJ/QqkwylWCONeVvqtYue8/y32/8847TJ48GYDFixdTu/bZ6/0kJiaSkpJCdnY2GRkZ+Pv7n/c9lCaZuZks3bOUmTtnsv7oerw9vOlVvxe3NbmNDjU6uOYqMf0YbPrKmRiObgNvf2dC6HA31G5vdwtupqrM33iQ8d9s51BSBje2qc0zNzSjTjX31ZUqVwnCHazc95/lvkeOHMnIkSPP/mHlc//99/PSSy+xe/dunn766VMd4qXd7qTdzNw5k/m75pOUmUT9KvV5osMT3Nz4ZtcMZFOFfb85m5C2zYOcDKjdDvq9Aa0GQoWy359TGmyKPc4LC7axdu8xWtapwpu3t6NTmPtHj1uCcDEr9332ct+HDx+mY8eOJCcn4+HhwRtvvMG2bduYN28eXl5eDBkyhNzcXLp06cIPP/xAjx49uPLKK9m+fTspKSmEhoby0Ucf0bt370J9Lu6SnZvNsn3L+GrnV6w+vBov8aJHvR78renf6FSzk2vmJkhNcI5ZWDfNOZq5QhXnYLUOd0OtNkV/PnNRjp7IYMKSHcxaF0t1fx/+e2trbu0QiqdHybibs3LfpkwqCX/v+5P3MytqFvOi55GYkUidSnUY2GQgtzS+hSC/oKI/oSrs+dnZhBS5AHKznB3NHe6BFv3Bp2w105VmmTm5TP1lD28tiyIr18Hwrg14uEdjKvu67tH0s7Fy38YUk2xHNiv2r+CrHV/x26Hf8BRPrgq9itua3kaX2l1cc7eQchQ2fOG8W0iMAd+q0GGY826hRouiP5+5aKrKd9uOMG5xJHsT0ri2eQjP9Y2gQVDJTN4uTRAicj0wCfAEPlTV8WesDwCmAI2ADGC4qm7JW1cN+BBoibPK13BV/c2V8RrzFwm74OB6ZxONb5XT//SpBB7OL/yDKQeZtXMWc6PnEp8eT42KNXio7UMMaDyAGv41ij4uhwNiljuTwvZFzoqo9brAVU9DxM02YU4JtPPICV5csI2V0fE0DqnEJ8M7071JsLvDOieXJQgR8QTeAXoBscBqEZmvqtvybTYG2KCq/UWkWd72PfPWTQKWqOpAEfEBLnpUkKq6bSIUU/yKpNnU4YBV/wfLXnA21RQgB+HnKoHMrFyRld7Of19XelTiNt8mdPOti1fcEUj+3HlF71v1vEmmUJIPwYbPYN2ncHwv+AXCZQ9A+7uc9Y9MiXM8LYuJ3+3ks9/34e/jyb9vjOCOy+vj7Vnyxym78g6iMxCtqjEAIjIduBnInyAigP8AqOp2EQkTkRpAOtAduCdvXRZQ8P/S8/D19SUhIYHq1atbkigHVJWEhITTHsW9YEmxMPcBZ3t+0z5w9bPOJJGRBJnJHDlxgDlxq5mdFMmR3HSCxZv7pBq35vpQOy0VjkVDxjrITD59roMCScGJo6BEEvMj7FwCmuucL6Hn89D8RquMWkLl5Dr44o99/O+7nSSnZ3PHZfV5rFcTAv3/WlanpHJlgqgD7M/3Oha47IxtNgIDgJUi0hmoD4QCuUAcMFVE2gBrgVGqmnrmSUTkfuB+cI48PlNoaCixsbHExcVd8hsypYOvr+9pT2hdkE0zYdETzi/2m96CdneCCA518OvBX/lq33J+iv2JXM2lS+0uPNPkNq6qexXeHgV0Lqo6y1VkJkNGct6fSWe8zrf85O8nDjnLZ598fTLJ+AdDl0ecdwvVC366zJQMK6PieXHhVnYeSaFLo+o8f2MEzWpWcXdYF8yVCaKgy/Uz7/3HA5NEZAOwGVgP5ADeQHvgEVX9XUQmAc8A//rLAVU/AD4A51NMZ6739vamQYMGl/A2TLmQfsyZGLbMhrqXQf/3ILAh8enxzIuex6ydsziQcoBA30DubnE3A8MHnn+uBRHwqej8qVzz4uI6lWROOGdV8yz+p1xM4e2JT2Xc4ki+23aEuoF+vH9nB66LqFFqWy9cmSBigfz/g0KBg/k3UNVkYBiAOD/B3Xk/FYFYVf09b9NZOBOEMUVv13KY9xCkHoUe/yTnikf49cgfzN4wiRWxK8jVXDrX7Mzo9qPpWa8n3sX5JZ0/yZgSKyUzh7d/iGbKyt14eQpPXd+U4V0b4OtduufDcGWCWA2Ei0gD4AAwGBiSf4O8J5XS8voY/g78lJc0kkVkv4g0VdUdODuut2FMUcpOh+9fgN/fhaAm7L/5DeYm7+DruX05mn6UQN9A7oq4i/7h/WlQ1e5CzV85HMrsdbH899sdxJ3I5Nb2oTx1fVNqVCnmOb9dxGUJQlVzRORh4Fucj7lOUdWtIvJA3vr3gObAJyKSizMB3JvvEI8An+c9wRRD3p2GMUXi0CaYcx+Z8dv5ofWNzK7ow++//AMP8aBr7a6MCR9D97rdC+5bMOVeamYO30ce4aOVu9kUm0S7etWYfFdH2tat5u7QilSZH0ltzGkcufDrm+z8+VXmVAtkYZXKJOWkUadSHW5pfAu3NL6Fmv4X2V9gyrSM7Fx+3HGUBZsOsSzyCBnZDupU8+PJ3k25qU1tPEpIeYwLZSOpjQFS4yL5ZuH9zMk6wubawXh7eNMztDsDwgdwWa3LXDPK2ZRqWTkOVkbHsXDjIZZuO0JKZg7V/X24rUNdbmxTm471A0ptYigMSxCmTFNVNh7dwJzfX2NJwkbSPYTG1UJ5quU99Gt0o2sqqJpSLSfXwaqYRBZuOsg3Ww6TlJ5NFV8v+raqxY1tanN5w0C8SsEgt6JgCcKUSYkZiSzYtYA5O2YSc2Ivfg4HfaQyA658kVYNepXaxw6Nazgcypq9x1iw8SDfbDlEfEoW/j6eXNeiJje2qUW3xsH4eJWPpJCfJQhTZuQ6cll1aBWzo2azfP9ychw5tMl28EJyMr07PYp/18fBo3Q/dmiKjqqyMTaJBRsPsmjTIQ4nZ+Dr7UHPZjW4sU0trm4aUuofU71UliBMqXco5RDzoucxN3ouh1IPUc2nKrf71GFA9CoaB4TDkOlQs5W7wzQlgKoSeegECzYdZOGmg+xPTMfbU7iqSQjP9mnGtc1r4F/BvhZPsk/ClErZudks37+cOdFz+PXArwBcUfsKHm/Ynx6/TMYnYTNc8TD0+Bd4l41n0s3Fiz6awoKNzqSwKy4VTw+ha+MgHu0RznUtalLVzx5nLoglCFOqxByPYU7UHBbELCAxI5EaFWswos0IbmlwI3U2TIeF/4RKNeGu+dDwKneHa9xoX0Ja3p3CISIPJSMClzUIZHi3BlzfoibVK1mRw/OxBGFKvLTsNJbuXcqcqDmsP7oeL/HimnrX0L9xf7rU7oLnsT0wczjEroZWf4M+E8CvmrvDNm5wKCmdRZsOsWDjQTbGJgHQvl41xt4YQZ9WtcrMCOfiYgnClEiqytaErcyOms03u78hNTuVsCphPNHhCfo16uecslPVOWHOkjHg6QUDp0DLW90duilmcScy+WaLMyms3nMMgJZ1qvDsDc3o27oWoQFWx+piWYIwJdLEtROZunUqvp6+9A7rzYDwAbQLaffn46kpR2H+o7DzG2hwFdzyLlSt496gTbE5npbFki2HWbDpIL/tSsCh0KRGJZ7o1YR+bWqX2Ck8SxtLEKbEWbBrAVO3TuXW8Ft5ouMTVPapfPoG2xfD/EecJbCvHw+dR1zYrGym1DqelsVbP0Tz6W97ycp1EFa9IiOvaUy/1rVpWrPy+Q9gLoglCFOibEvYxgu/vUCnmp147vLnTi+Wl5kC3z4L6z5xPrY6YCGENHdfsKbYZGTnMu3XPbyzPJqUzBxu61CXO6+oT4vaVWzQowtZgjAlRkJ6AqOWjyLQN5DXrnrt9OSw/w+Ycz8c2wPdHoOrx4BX6Zm60Vwch0OZv/EgE77dwYHj6VzTNJhnbmhudwvFxBKEKRGyHdn8Y8U/OJZxjE9u+IRA30DnitxsWPEq/Pw6VA2FYYuhfhf3BmuKxa+74vnP4u1sPpBEi9pVmDCwNV0aB7k7rHLFEoQpEV5f8zprjqzhlW6vEFE9wrkwPgrm3AcH10PbO5z9Db6lb15fc2F2HjnB+G+288P2o9Sp5sfEQW24uU2dMl01taSyBGHcbl70PD6P/Jw7I+7kxro9YMc3ELUUNnwJ3n7wt08h4iZ3h2lc7GhyBv/7bidfrdmPfwUvnrmhGfd0CSv39ZDcyRKEcastcZt56bcXucyvDo9H/gpLJkBuFnj7Q7O+0HscVLYJfMqy1Mwc3v8phsk/xZDjcHB3lzAe7RFOgL/1MbmbJQhT/LLTYc8vxO9YwOi4HwjKzWHC7t/xCgyHzvdDeC+odwV4WSmEsiwn18GMNfuZ+F0U8SmZ9G1di6d6N6V+dRvDUFKcN0GIyGvAVFXdWgzxmLIqcTdEf+9sOtr9M9k56TxRqyZJvhX4tNGdBNw6BALC3B2lKQaqyrLIo4xfsp3ooyl0Cgtg8l0daFfPJm8qaQpzB7Ed+EBEvICpwJeqmuTasEypl5MJe3+FqO+cSSEhyrk8oAF0uJv/eiSz7vBKXr1yPM0a9nFvrCVcYmoWH62MIdcB3cOD6BAWQAWv0tkuvyn2OOMWRfL77kQaBvnz/p0duC6iho1lKKHOmyBU9UPgQxFpCgwDNonIL8BkVV3u6gBNKXJ8P0R/50wKMSsgOxU8K0BYN+h0L4RfB9UbMTdqLtN/fZ57WtxDH0sOZ3VycNjby6NJzczBQ4T3VuzCz9uTyxsGcmV4MN2bBNEouFKJ/4Ldn5jGf7/dwYKNB6nu78NLN7dgcOd6eJeTqTtLq0L1QYiIJ9As7yce2Ag8LiIjVHWwC+MzJVluNuxb5bxDiP4ejm5zLq9aD9oMdiaEBleCz59typviNvHSqpe4otYVjGo/yk2Bl2yqyoJNh/jvku3EHnMODhvTpzm1qvnxe0wCP+2M4+eoeJbvcH7etar6cmV4EN2bBNO1UVCJ6tw9npbF2z9E88lve/HwgIevacyIqxpS2dfmXygNRFXPvYHI/4CbgGXAR6r6R751O1S1qWtDLLyOHTvqmjVr3B1G2ZZ86M++hJgfITMZPLyh/hXOhBB+HQQ1gQKuaOPT4xm0YBDent7M6DeDqhWqFn/8JdyaPYm8tCiSjfuP07xWFZ7r05xu4QUPDtufmMbK6Hh+2hnHL9HxJGfkIAKt61TlyvBgrgwPon39ALdcpWfm5PLJr3t5e3k0yRnZ3NYhlMd7NaVmVSu3XdKIyFpV7VjgukIkiOHAdFVNK2Bd1ZLUH2EJwgVyc+DAGmdCiFoKhzc7l1eu7XzaKLyXs5rqeQawZedmc+/Se9meuJ1Pb/iUpoEl5rqiRNgTn8qrS7bzzZbD1KhSgX9c15QB7UPxLOTgsJxcB5sOJPHzznh+jopj/f7j5DoUfx9PrmgURPcmQVwZHkxY9YoubY5yOJQFm5ylMWKPpXNVk2Ce7dOMZjVtgGNJda4EUZgmpmPAqftBEakGXK2q80pScjBFKCXuz7uEXcsgIwnEE+pdDj3HOu8SarQo8C7hbMb/MZ71R9cz4aoJlhzyOZ6WxZvLovl01R68PT147Nom3Ne9ARV9LuwJdC9PD9rXC6B9vQBGXRtOUno2v+1K4OeoOH6KiuP7yCMA1A30c/ZdhAdxRaOgIp1qc1VMAq8sjmRTbBIRtarw2b2tz3r3Y0qHwtxBbFDVtmcsW6+q7VwZ2MWwO4hL5HDATxNgxXhQB/iH/HmX0PCai56lbdbOWbzw2wsMbzmcxzo8VrQxl1KZObl8+tte3lwWRUpmDoM61eWxa5sQ4qIZz/bEp+Yli3h+25VASmYOHgJt61aje5NgrgwPpk1oVbwuojkqKq80xrLtR6ld1ZcnrmtK/3ZWGqO0uNQmpk2q2vqMZZtVtVURxlgkLEFcgoxkmPsA7FgErW6DKx6Gmq0veZ6FDUc3MOzbYVxW8zLe6fkOnh6l8/HMoqKqLN58mFeXbGdfYhrdmwQzppibYLJzHWzYf5yfdjoTxqbY46hCZV8vujYKyksYQdQNPPdMbEdPZDDxuyhmrN6Hv48XD13TmGFdrTRGaXOpCWIKcBx4B1DgESBAVe8p2jAvnSWIixQfBdOHQMIu6P0KXDbigpqPzuZo2lEGLxyMr5cvX/b9stx3Sq/de4xxi7axbt9xmtWszLN9mnNVk2B3h8XxtCx+ic5rjtoZx8GkDAAaBPk7n44KD+byRtWpVMHZ7JWamcPkn2P44KcYsnIcDL28Po/2DCewBD09ZQrvUhOEP/Av4FpAgKXAy6qaWtSBXipLEBdhxxJnxVRPb7htmvOx1CKQlZvFsG+HEXUsis/7fE54QHiRHLc02p+Yxvgl21m06RDBlSvwj+uaMLBD3UJ3QBcnVWVXnLM56ue85qj07Fy8PIT29QNoE1qVeRsOEncikz6tavJU72aE2fSepdoldVLnJYJnLvLE1wOTAE/gQ1Udf8b6AGAK0AjIAIar6pZ86z2BNcABVe13MTGYs3A44OfXYPk4qNUGBn0G1eoVyaFVlVd+f4VNcZv439X/K7fJISktm7eXRzHtV+cYgEd7hjOie0P8K5TcEmgiQuOQSjQOqcSwrg3IzMll7d5j/BzlfDpq8s+76VA/gPeGdqBDfSuNUdYVphZTMPAU0AI41YOmqj3Os58nzmapXkAssFpE5qvqtnybjQE2qGp/EWmWt33PfOtHAZGAPSNXlDJPOPsbti+E1oPgxknOstpFZObOmcyOms19re6jV/1eRXbc0iIrx8Fnq/by5g9RJKVnM7B9KE9cVzrHAFTw8qRLoyC6NAri6eubkZGdSwUvjxI/ctsUjcJcynwOzAD6AQ8AdwNxhdivMxCtqjEAIjIduBnInyAigP8AqOp2EQkTkRqqekREQoG+wDjg8UK+H3M+8dF5/Q3R0Ps/cPmDRdLfcNK6I+v4zx//4co6VzKy7cgiO25poKp8u/UI47+JZE9CGt0aBzGmT3Miaped6xvrgC5fCpMgqqvqRyIySlVXACtEZEUh9qsD7M/3Oha47IxtNgIDgJUi0hmoD4QCR4A3cN65nHPyWRG5H7gfoF69omkiKbN2fguz7wMPT7hrHjToXqSHP5x6mMd/fJw6leowvvv4cvXE0ob9x3llUSR/7EkkPKQSU+/pxNVNg+1K25RqhUkQ2Xl/HhKRvsBBnF/i51PQ/4wze8THA5NEZAOwGVgP5IhIP+Coqq4VkavPdRJV/QD4AJyd1IWIq/xxOJxzOi8fBzVbweDPi6y/4aTM3Ewe//Fx0nPS+aj3R1TxKTtXzecSeyyN/y7ZwfyNBwmq5MO4/i0Z1LHuRY0nMKakKUyCeFlEqgJPAG/h7A8ozGinWKBuvtehOJPLKaqajLNCLOK81Nqd9zMYuElE+uDs96giIp+p6tBCnNfkl3kC5j0IkQug1d+c/Q0+536+/UKpKuNWjWNz/GbeuPoNGlVrVKTHL4mSM7J5Z3k0U3/Zg+AsQvfA1Y1OPQpqTFlwzn/NeR3N4aq6EEgCrrmAY68GwkWkAXAA55f+kDOOXw1IU9Us4O/AT3lJ49m8H/LuIP5hyeEiJOxy9jfE74TrxsEVI4u0v+Gk6TumMzd6LiNaj6Bn/Z7n36EUy8518MXv+5i0LIrE1CwGtK/Dk72bUqtq0XXyG1NSnDNBqGquiNwETLzQA6tqjog8DHyL8zHXKaq6VUQeyFv/HtAc+EREcnF2Xt97oecxZxH1Hcy619nfcOdcaHi1S06z5vAa/vvHf7kq9CoeavuQS85REqgq30ce5T/fRBITl8rlDQP5Z98IWtYp34P/TNlWmIFy44CqOJ9kOjU4TlXXuTa0C2cD5QBVZ3/DDy9DzZYw6HMIqO+SUx1OPcyghYOo4lOFL/p+QWWfcz5PUGptjk1i3OJtrIpJpGGwP2NuaE7P5iHWAW3KhEut5tol788X8y1T4JzjIIwbZKbk9TfMh5YD4aa3iry/4aSMnAxGLx9NZm4mk3pMKpPJITMnl5cXRvLpqr3OWdBuacngTnVtFjRTbhRmJPWF9DsYd0nYBdPvgPgdcN3LzmJ7LrrCVVVeWvUSWxO28uY1b9KwakOXnMedjiZn8ODn61i79xjDuzZgdK9wqtgsaKacKcxI6ucLWq6qLxa03LhB1PcweziIBwydA41cm9O/2P4F83fN56E2D3FNvbJ3/bBh/3FGfLqG5PQc3h7Sjn6ta7s7JGPcojBNTPmL8vniHFEd6ZpwzAVRhZUTYdmLzgl8Bn8OAWEuPeXqw6uZsHoC19S9hhFtRrj0XO4wa20sY+ZuJqRyBWY/2KVMjYI25kIVponp9fyvReQ1YL7LIjKFk5kCX4+EbfOgxQC4+W3wcW1VzYMpB3nixyeoV6Uer3R7BQ8pO23x2bkOxi2K5ONf99ClUXXeHtLeylebcu9iRvVUBMpeo3Npkhjj7G+I2w69XoQuj7qsv+Gkk53S2Y5sJl0ziUo+lVx6vuKUmJrFyM/X8VtMAsO7NmBMn2Y2EtoYCtcHsZk/S2R4AsGc/kSTKU7R3zvHNwAMnQ2NXP8wmarywm8vsD1xO2/3fJsGVRu4/JzFZevBJO7/ZC1xKZm8flsbbu1QmCoyxpQPhbmDyD8PQw5wRFVzXBSPORtV+OUNZ39DcHNnf0Ng8XxRf7rtUxbGLOThtg/TPbRoC/y504KNB3ly1kaq+fkwc8QVtKlbzd0hGVOiFCZB1AK2quoJABGpJCItVPV314ZmTslKdfY3bJ0LLfrDze+4vL/hpFWHVvG/tf/j2nrXcl/r+4rlnK6W61AmfLuD91bsomP9AP5vaHtCKpe+uRqMcbXCJIh3gfb5XqcVsMy4SuLuvP6GSLj2Beg6yuX9DScdSDnAkyueJKxKGC93e7lMdEonpWXz6PT1rNgZx5DL6vHvG1vg41X635cxrlCYBCGarx6HqjpExEpWFoddP8DMYYDCHTOh8bXFdur0nHRGLx9NriOXST0m4e9d+ucdjjpygvs+WcOB4+mM69+SOy5zTQkSY8qKwnzRx4jIozjvGgAeAmJcF5JBFX59E77/NwQ3y+tvKL4Hx1SVsb+OZUfiDt7p+Q71q5T+L9KlWw/z2IwN+Pl48cV9l9MpLNDdIRlT4hUmQTwAvAn8E+fTTMvIm8HNuEBWKsx/BLbMhoib4eb/gwrF+0jptK3T+Gb3N4xqP4orQ68s1nMXNYdDefOHKN74PorWoVV5/84OVprbmEIqzEC5ozjncjCudmyPs7/hyFboORa6PXbO/gZVJduRTXpOOuk56aTlpJGRk3Hqdf7fC/o5c9u0nDTSc9I5lHqIXvV7cW/L0l19PSUzh8dnbGDptiMMaF+HV/q3sjmVjbkAhRkHMQ0YparH814HAK+r6nAXx1auZB/ezKyZt3LEQ8noMoR0zyTSf3ra+QWem++LPTudjNw/v9gd6rig83h7eOPn5Yevly8VvSri5+WHn5cflXwqEVwxGD8vP3pX7M2I1iNKdTnrPfGp3PfJGmLiU3m+XwTDuoaV6vdjjDsUpomp9cnkAKCqx0SknetCKn8yj+3lHwtu58cqFfAST/ziN+B33A8/bz98PX3x8/LD38uf6r7VT32hF/Tj6+V73nVeHmX/+YIfdxzl0S/X4+EhfDK8M10bB7k7JGNKpcJ8W3iISICqHgMQkcBC7mcKIS31KKPm3sIqH+G5Zncz+LJ/uDukUktVef+nGP67ZDtNalRm8l0dqRvomvkwjCkPCvNF/zrwq4jMynt9G/CK60IqP06kH2PkrL5s9Mjm5cZDuNmSw0VLz8rlqdmbWLDxIH1b12LCwNZU9LHrGGMuRWE6qT8RkTU4Z5ATYICqbnN5ZGXc8fRjPDC7Lzs0nVfr3cj1Xce4O6RSa39iGiM+XUvk4WSeur4pD17VyPobjCkChbrEyksI20SkEXC7iHylqi1dG1rZFZ8ez31z+7MvO5k3gq/kqh7/cXdIpdZvuxIY+cU6snMdTLm7E9c0C3F3SMaUGeetMSAitURktIj8AWzFWdH1dpdHVkYdTj3MsK9v5UBmIu9UjOCqvv/n7pBKJVXl4192M/Sj3wn09+HrkV0tORhTxM56ByEi9+FMBKHAV8Dfga9V9YViiq3M2Z+8n78vHkpyejzvSy3a3fppsdVVKksysnP517wtzFwby7XNazBxUBsq23zRxhS5czUxvQP8BgxR1TUAIqLn2N6cQ8zxGO5bcg+Z6Ql8mFGRFvfMBK8K7g6r1DmclMGIz9aycf9xHu0Zzuie4Xh4WJI1xhXOlSBq43xi6X8iUgPnXYRdpl2E7YnbGbH0PiT9GFOP5xA+bDb4Bbg7rFJn7d5EHvhsHamZObw3tD3Xt6zl7pCMKdPO2gehqvGq+q6qdgd6AknAURGJFBF7zLWQNsVtYviS4fikJzHt6HHCB82AavXcHVapM/2PfQz+YBUVfTyZ+1BXSw7GFINCFcJX1VhVfU1VOwC3AJkujaqMWH14NfctvY9q2RlMO3CI+gOmQu227g6rVMnKcfCveVt4Zs5mLm9Ynfkju9G0ZmV3h2VMuXDBI4lUdQdgHdXn8cuBXxi1fBR11JPJ+/YQcsPrEN7L3WGVKnEnMhn5+Tr+2JPIiO4Neer6Znhaf4MxxcaGmrrAsn3LeHLFkzTyqsz7UZsI7DIaOg5zd1ilxoHj6fwSFc/E73eSmJrFpMFtubltHXeHZUy5YwmiiC2OWcyYlWNo4VeTdyNXUaXFQOjxL3eHVaIdT8vit10JrIyO59ddCeyOTwWgfvWKzH6wCy3rVHVzhMaUT4Up971MVXueb9lZ9r0emIRzcN2Hqjr+jPUBwBSgEZABDFfVLSJSF/gEqAk4gA9UdVIh35PbzImaw79//Tcdq4bz1paf8a/bFW5+BzxszuP8MrJzWb0n0ZkQohPYcjAJVfD38eTyhtUZenl9ujUOokmNSlYywxg3OtdAOV+gIhCU90V+8n9qFZyPwJ6TiHjiHEvRC4gFVovI/DPqOI0BNqhqfxFplrd9TyAHeEJV14lIZWCtiHxXkmtAfR75OeP/GE+34HZM3PQjvtXCYPBnNtYByMl1sPlAEr/uSmBlVDxr9x4jK9eBt6fQrl4Ao3s2oVt4dVqHVsPb05KpMSXFue4gRgCjcSaDtfyZIJJxfpGfT2cgWlVjAERkOnAzkP9LPgL4D4CqbheRMBGpoaqHgEN5y0+ISCRQ54x9S4wPN3/IpHWTuLZ2N17dshIfL18YOqvcjnVQVXbFpfJLdDwro+NZFZPAiYwcAJrXqsLdXerTpXEQncMC8a9grZzGlFRn/d+Z16QzSUQeUdW3LuLYdYD9+V7HApedsc1GYACwUkQ6A/VxlvY4cnIDEQkD2gG/F3QSEbmfvDmy69Ur3vEFqspb699i8ubJ9Kt/PS/tWIVXWgIMW1TuxjocTsrgl+h4ftkVzy/R8RxJdj4JHRrgR99WtejaOIgrGlUnqJLdURlTWhTm8u2wiFTOu5L/J9AeeFlV151nv4Iaj88s1TEeZxLaAGwG1uNsXnIeQKQSMBsYrarJBZ1EVT8APgDo2LFjsZUCUVX+u/q/fBb5GQMb38q/dm/B4/AWuH0G1C77E+4lZ2SzaldCXlJIIPpoCgCB/j5c0ag63RoH0bVREPWq24Q9xpRWhUkQ/1LVmSLSDegNvAa8y1/vBs4UC9TN9zoUOJh/g7wv/WEA4uyN3J33g4h440wOn6vqnELEWWxyHbm8tOolZkfNZmjzoTx15BAS/R30mwhNrnN3eC6RkZ3Lun3HnAkhOoFNscdxKPh5e9K5QSB/6xhK18ZBNK9ZxWojGVNGFCZB5Ob92Rd4V1W/FpF/F2K/1UC4iDQADgCDgSH5NxCRakCaqmbhrBb7k6om5yWLj4BIVf1fod5JMclx5PDcyudYvHsx97e+n4dTspG1U6HbY9BxuLvDKzK5DmXbweRTTUZ/7E4kM8eBp4fQJrQqD1/TmC6Ng2hXrxoVvDzdHa4xxgUKkyAOiMj7wLXAqyJSgUKU6FDVHBF5GPgW52OuU1R1q4g8kLf+PaA58ImI5OLsgL43b/euwJ3A5rzmJ4Axqrq48G+t6GXlZvHUT0+xbN8yRrUfxd+pBsvuhZYDocfz7gytyMTEpfD60p38siue42nZADSpUYkhl9Wja6MgLmsYaKW1jSknRPXczfYiUhG4HtisqlEiUgtopapLiyPAC9GxY0dds2aNS46dnpPOYz8+xi8HfuGZzs9wh199+LQ/hHaGO+eUicdZl+84yqNfrsdDhF4RNejWOIgujaoTUsXX3aEZY1xERNaqaseC1hVmTuo0ETkKdAOicHYiRxVtiCVbanYqDy97mLVH1vJilxfpXy0CPuoFAQ3KxFgHVeX9n2J4dcl2mteswgd3dSA0wDqXjSnvCjOSeizQEWgKTMU5J8RnOJuByrykzCQe+v4htiZs5dXur3JDUHv48FrwrAB3zCz1Yx3Ss3J5evYm5m88SN/WtZgwsDUVfWxsgjGmcH0Q/XGOQ1gHoKoH80Y3l3kJ6QmM+G4EMUkx/O/q/9GjRmf4uC+kxcM9iyCgvrtDvCQHjqcz4tM1bD2YzJO9m/LQ1Y2stIUx5pTCJIgsVdWT042KiL+LYyoRjqQe4b7v7uNQyiHe7vE2XWp2hhl3wOFNMPhLqNPe3SFektV7Ennws7VkZDv48K6O9Gxew90hGWNKmMIkiK/ynmKqJiL3AcOBD10blnsdSDnA37/9O8cyj/Fer/foENIeFj0BO5dA3/9B0+vdHeIl+eL3fYydv4XQgIpMv78DjUPKxQ2hMeYCFaaT+jUR6YWzBlNT4HlV/c7lkbnJnqQ9/H3p30nPSWdyr8m0Cm4FK9+ANR9B19HQ6d7zHaLEyspx8OLCrXy2ah9XNQnmzdvbUdXPHlk1xhSsMJ3Ur6rq08B3BSwrU3Ye28n9S+9HUab0nkLTwKaweRZ8PxZa3go9x7o7xIsWn5LJQ5+v44/diYy4qiFP9bbZ2Ywx51aY2soFzZN5Q1EH4m5b47cy/NvheHp4MvX6qc7ksOcXmPcg1O8Kt7xbaud12HIgiZvf/oWN+48zaXBbnr2huSUHY8x5nWs+iAeBh4CGIrIp36rKwC+uDqw4rTuyjoeWPUS1CtX48LoPCa0cCnE7YPrtEBAGg0rvWIcFGw/y5KyNBFT0YdYDXWgVarOzGWMK51xNTF8A3+Ccr+GZfMtPqGqiS6MqRsczjvPQsocI9gtm8nWTqelfE04cgc8HgqePc6xDxUB3h3nBch3K60t38H8/7qJj/QDeHdqB4MqlM8kZY9zjXPNBJAFJwO3FF07xq+ZbjXHdxtEmuA1BfkGQlQpf/A1ST451CHN3iBcsOSOb0dM38MP2o9zeuR4v3NQCH6/S2TxmjHEfGzIL9KyXN712bg7MGp431uGLUjnWYVdcCvd9soZ9CWm8fEtLhl5eugfzGWPcxxLESarwzVPOsQ59XoOmpa8f/mSxPW9PDz7/+2Vc1rC6u0MyxpRiliBO+vXNvLEOo6Dzfe6O5oJYsT1jjCtYggDYMhu+ex5aDICe/3Z3NBfEiu0ZY1zFvknSEmH+o1DvilI31sGK7RljXMkSRMVAGDIDQiLAu/RMjGPF9owxrmYJAiCsm7sjuCBWbM8YUxwsQZQiVmzPGFOcLEGUElZszxhT3CxBlAJbDiQx4tO1xKdkMmlwW25uW8fdIRljygFLECXc/I0HecqK7Rlj3MASRAmV61BeW7qDd3/cRaewAP7vDiu2Z4wpXpYgSqDkjGxGfbme5TvirNieMcZtLEGUMFZszxhTUliCKEGs2J4xpiSxBFFC/Lornns/Xk0zK7ZnjCkhLEGUAAkpmYyevoGwIH++euAKKlWwvxZjjPu5tOdTRK4XkR0iEi0izxSwPkBE5orIJhH5Q0RaFnbfskJVeXLWJo6nZfPW7e0sORhjSgyXJQgR8QTeAW4AIoDbRSTijM3GABtUtTVwFzDpAvYtE6b8socfth9lTJ9mtKhtYxyMMSWHK+8gOgPRqhqjqlnAdODmM7aJAJYBqOp2IExEahRy31Jvy4Ekxn8TybXNa3B3lzB3h2OMMadxZYKoA+zP9zo2b1l+G4EBACLSGagPhBZy31ItNTOHR75cT3X/CkwY2NrmcTDGlDiuTBAFfePpGa/HAwEisgF4BFgP5BRyX+dJRO4XkTUisiYuLu4Swi1ez3+9lb0JqbwxuC0B/j7uDscYY/7ClT2isUDdfK9DgYP5N1DVZGAYgDgvoXfn/VQ83775jvEB8AFAx44dC0wiJc289QeYvS6WR3uGc7mNdTDGlFCuvINYDYSLSAMR8QEGA/PzbyAi1fLWAfwd+CkvaZx339JqT3wqz83dTOewQB7t0djd4RhjzFm57A5CVXNE5GHgW8ATmKKqW0Xkgbz17wHNgU9EJBfYBtx7rn1dFWtxycpx8Oj09Xh5evDG4LZ4eVp9JWNMyeXSh+5VdTGw+Ixl7+X7/TcgvLD7lnYTvt3Optgk3hvagdrV/NwdjjHGnJNdwhaT5TuOMvnn3Qy9vB7Xt6zp7nCMMea8LEEUg6PJGfzjq400q1mZf/Ytk+P9jDFlkNV1cDGHQ3n8q42kZuUw/fbL8fX2dHdIxhhTKHYH4WLv/bSLldHxjL2xBeE1Krs7HGOMKTRLEC60bt8xXl+6k76tajG4U93z72CMMSWIJQgXSUrP5tEv11Ozii+vDGhlpTSMMaWO9UG4gKoyZu5mDiVl8NWIK6jq5+3ukIwx5oLZHYQLzFi9n0WbDvF4ryZ0qB/g7nCMMeaiWIIoYlFHTvDvBVvp2rg6D17VyN3hGGPMRbMEUYQysnN55Mv1+Pt4MfFvbfHwsH4HY0zpZX0QReiVxZFsP3yCqcM6EVLF193hGGPMJbE7iCLy7dbDfPLbXv7erQHXNA1xdzjGGHPJLEEUgYPH03lq1iZa1anKU9c3c3c4xhhTJCxBXKKcXAejp28gJ9fBW7e3w8fLPlJjTNlgfRCX6K0fovljTyITB7UhLMjf3eEYY0yRscvdS7AqJoG3fohiQPs69G8X6u5wjDGmSFmCuEjHUrMYPX0D9QIr8uLNLd0djjHGFDlrYroIqsqTszaRkJrJnAe7UqmCfYzGmLLH7iAuwie/7eX7yCM8fX0zWoVWdXc4xhjjEpYgLtC2g8mMWxzJNU2DubdbA3eHY4wxLmMJ4gKkZeXw8JfrqObnzWu3tbES3saYMs0azy/Av+dvZXd8Kp/fexnVK1VwdzjGGONSdgdRSPM3HuSrNbE8dHUjujQOcnc4xhjjcpYgCmFfQhpj5mymfb1qjL62ibvDMcaYYmEJ4jyycx08Mn09IjBpcDu8Pe0jM8aUD9YHcR6vLd3Bxv3H+b872lM3sKK7wzHGmGJjl8Pn8NPOON5fEcPtnevRp1Utd4djjDHFyhLEWcSdyOTxrzbSpEYlnu8X4e5wjDGm2FkTUwEcDuWJmRs5kZHN53+/DD8fT3eHZIwxxc6ldxAicr2I7BCRaBF5poD1VUVkgYhsFJGtIjIs37rH8pZtEZEvRaTY5vD8cGUMP+2M41/9Imhas3JxndYYY0oUlyUIEfEE3gFuACKA20XkzLaakcA2VW0DXA28LiI+IlIHeBToqKotAU9gsKtizW/j/uP8d8kOrm9Rkzsuq1ccpzTGmBLJlXcQnYFoVY1R1SxgOnDzGdsoUFmcNSsqAYlATt46L8BPRLyAisBBF8YKwImMbB75cj0hlSsw/tZWVkrDGFOuuTJB1AH253sdm7csv7eB5ji//DcDo1TVoaoHgNeAfcAhIElVlxZ0EhG5X0TWiMiauLi4iw5WVfnnvC3EHktj0u3tqFbR56KPZYwxZYErE0RBl996xuvewAagNtAWeFtEqohIAM67jQZ56/xFZGhBJ1HVD1S1o6p2DA4OvuhgZ62N5esNBxl9bRM6hQVe9HGMMaascGWCiAXq5nsdyl+biYYBc9QpGtgNNAOuBXarapyqZgNzgC6uCnRXXArPf72VyxsGMvKaxq46jTHGlCquTBCrgXARaSAiPjg7meefsc0+oCeAiNQAmgIxecsvF5GKef0TPYFIVwSZmZPLI1+sx9fbgzcGtcPTw/odjDEGXDgOQlVzRORh4FucTyFNUdWtIvJA3vr3gJeAj0VkM84mqadVNR6IF5FZwDqcndbrgQ9cEWeuQ2lWqzKP92pCzarF9iStMcaUeKJ6ZrdA6dWxY0dds2aNu8MwxphSQ0TWqmrHgtZZqQ1jjDEFsgRhjDGmQJYgjDHGFMgShDHGmAJZgjDGGFMgSxDGGGMKZAnCGGNMgSxBGGOMKVCZGignInHA3ovcPQiIL8JwSjP7LE5nn8fp7PP4U1n4LOqraoGVTstUgrgUIrLmbKMJyxv7LE5nn8fp7PP4U1n/LKyJyRhjTIEsQRhjjCmQJYg/uaRabClln8Xp7PM4nX0efyrTn4X1QRhjjCmQ3UEYY4wpkCUIY4wxBSr3CUJErheRHSISLSLPuDsedxKRuiKyXEQiRWSriIxyd0zuJiKeIrJeRBa6OxZ3E5FqIjJLRLbn/Ru5wt0xuZOIPJb3/2SLiHwpImVuSspynSBExBN4B7gBiABuF5EI90blVjnAE6raHLgcGFnOPw+AUbhoPvRSaBKwRFWbAW0ox5+LiNQBHgU6qmpLnNMqD3ZvVEWvXCcIoDMQraoxqpoFTAdudnNMbqOqh1R1Xd7vJ3B+AdRxb1TuIyKhQF/gQ3fH4m4iUgXoDnwEoKpZqnrcrUG5nxfgJyJeQEXgoJvjKXLlPUHUAfbnex1LOf5CzE9EwoB2wO9uDsWd3gCeAhxujqMkaAjEAVPzmtw+FBF/dwflLqp6AHgN2AccApJUdal7oyp65T1BSAHLyv1zvyJSCZgNjFbVZHfH4w4i0g84qqpr3R1LCeEFtAfeVdV2QCpQbvvsRCQAZ2tDA6A24C8iQ90bVdEr7wkiFqib73UoZfA28UKIiDfO5PC5qs5xdzxu1BW4SUT24Gx67CEin7k3JLeKBWJV9eQd5SycCaO8uhbYrapxqpoNzAG6uDmmIlfeE8RqIFxEGoiID85OpvlujsltRERwtjFHqur/3B2PO6nqs6oaqqphOP9d/KCqZe4KsbBU9TCwX0Sa5i3qCWxzY0jutg+4XEQq5v2/6UkZ7LT3cncA7qSqOSLyMPAtzqcQpqjqVjeH5U5dgTuBzSKyIW/ZGFVd7L6QTAnyCPB53sVUDDDMzfG4jar+LiKzgHU4n/5bTxksu2GlNowxxhSovDcxGWOMOQtLEMYYYwpkCcIYY0yBLEEYY4wpkCUIY4wxBbIEYUwJICJXW8VYU9JYgjDGGFMgSxDGXAARGSoif4jIBhF5P2++iBQReV1E1onIMhEJztu2rYisEpFNIjI3r34PItJYRL4XkY15+zTKO3ylfPMtfJ43QtcYt7EEYUwhiUhzYBDQVVXbArnAHYA/sE5V2wMrgLF5u3wCPK2qrYHN+ZZ/Dryjqm1w1u85lLe8HTAa59wkDXGObDfGbcp1qQ1jLlBPoAOwOu/i3g84irMc+Iy8bT4D5ohIVaCaqq7IWz4NmCkilYE6qjoXQFUzAPKO94eqxua93gCEAStd/q6MOQtLEMYUngDTVPXZ0xaK/OuM7c5Vv+ZczUaZ+X7Pxf5/GjezJiZjCm8ZMFBEQgBEJFBE6uP8fzQwb5shwEpVTQKOiciVecvvBFbkza8RKyK35B2jgohULM43YUxh2RWKMYWkqttE5J/AUhHxALKBkTgnz2khImuBJJz9FAB3A+/lJYD81U/vBN4XkRfzjnFbMb4NYwrNqrkac4lEJEVVK7k7DmOKmjUxGWOMKZDdQRhjjCmQ3UEYY4wpkCUIY4wxBbIEYYwxpkCWIIwxxhTIEoQxxpgC/T8Fv6bCcPNpyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_relu_results, label='CNN-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_bn_results, label='CNN-ReLU-BN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_1x1_results, label='CNN-ReLU-BN-1x1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:51:31.454656Z",
     "start_time": "2021-03-25T03:51:31.450681Z"
    }
   },
   "outputs": [],
   "source": [
    "class ResidualBlockE(nn.Module):\n",
    "    def __init__(self, channels, kernel_size=3, leak_rate=0.1):\n",
    "        \"\"\"\n",
    "        channels: how many channels are in the input/output to this layer\n",
    "        kernel_size: how large of a filter should we use\n",
    "        leak_rate: paramter for the LeakyReLU activation function\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "        #how much padding will our convolutional layers need to maintain the input shape\n",
    "        pad = (kernel_size-1)//2\n",
    "        \n",
    "        #Define the conv an BN layers we will use in a sub-network, just 2 hidden layers of conv/BN/activation\n",
    "        self.F = nn.Sequential(\n",
    "            nn.Conv2d(channels, channels, kernel_size, padding=pad),\n",
    "            nn.BatchNorm2d(channels),\n",
    "            nn.LeakyReLU(leak_rate),\n",
    "            nn.Conv2d(channels, channels, kernel_size, padding=pad),\n",
    "            nn.BatchNorm2d(channels),\n",
    "            nn.LeakyReLU(leak_rate),\n",
    "        )\n",
    "     \n",
    "    def forward(self, x):\n",
    "        return x + self.F(x) #F() has all the work for the long path, we just add it to the input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:51:31.461581Z",
     "start_time": "2021-03-25T03:51:31.455794Z"
    }
   },
   "outputs": [],
   "source": [
    "class ResidualBottleNeck(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size=3, leak_rate=0.1):\n",
    "        super().__init__()\n",
    "        #how much padding will our convolutional layers need to maintain the input shape\n",
    "        pad = (kernel_size-1)//2\n",
    "        #The botteneck should be smaller, so output/4 or input. You could also try changing max to min, its not a major issue. \n",
    "        bottleneck = max(out_channels//4, in_channels)\n",
    "        #Define the three sets of BN and convolution layers we need. \n",
    "        #Notice that for the 1x1 convs we use padding=0, because 1x1 will not change shape! \n",
    "        self.F = nn.Sequential(\n",
    "            #Compress down\n",
    "            nn.BatchNorm2d(in_channels),\n",
    "            nn.LeakyReLU(leak_rate),\n",
    "            nn.Conv2d(in_channels, bottleneck, 1, padding=0),\n",
    "            #Normal layer doing a full conv\n",
    "            nn.BatchNorm2d(bottleneck),\n",
    "            nn.LeakyReLU(leak_rate),\n",
    "            nn.Conv2d(bottleneck, bottleneck, kernel_size, padding=pad),\n",
    "            #Expand back up\n",
    "            nn.BatchNorm2d(bottleneck),\n",
    "            nn.LeakyReLU(leak_rate),\n",
    "            nn.Conv2d(bottleneck, out_channels, 1, padding=0)\n",
    "        )\n",
    "\n",
    "        #By default, our shortcut will be the identiy function - which simply returns the input as the output\n",
    "        self.shortcut = nn.Identity()\n",
    "        #If we need to change the shape, then lets turn the shortcut into a small layer with 1x1 conv and BM\n",
    "        if in_channels != out_channels:\n",
    "            self.shortcut =  nn.Sequential(\n",
    "                    nn.Conv2d(in_channels, out_channels, 1, padding=0), \n",
    "                    nn.BatchNorm2d(out_channels)\n",
    "                )\n",
    "\n",
    "    def forward(self, x):\n",
    "        # shortcut(x) plays the role of \"x\", do as little work as possible to keep the tensor shapes the same.\n",
    "        return self.shortcut(x) + self.F(x) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:51:31.475237Z",
     "start_time": "2021-03-25T03:51:31.462828Z"
    }
   },
   "outputs": [],
   "source": [
    "cnn_res_model = nn.Sequential(\n",
    "    ResidualBottleNeck(C, n_filters), #BottleNeck to start because we need more channels. Its also common to start with just one normal hidden layer before starting residual blocks. \n",
    "    nn.LeakyReLU(leak_rate), #We are inserting a activation after each residual. This is optional. \n",
    "    ResidualBlockE(n_filters),\n",
    "    nn.LeakyReLU(leak_rate),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    ResidualBottleNeck(n_filters, 2*n_filters),\n",
    "    nn.LeakyReLU(leak_rate),\n",
    "    ResidualBlockE(2*n_filters),\n",
    "    nn.LeakyReLU(leak_rate),\n",
    "    nn.MaxPool2d((2,2)),\n",
    "    ResidualBottleNeck(2*n_filters, 4*n_filters),\n",
    "    nn.LeakyReLU(leak_rate),\n",
    "    ResidualBlockE(4*n_filters),\n",
    "    nn.LeakyReLU(leak_rate),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(D*n_filters//4, classes),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:55:51.579797Z",
     "start_time": "2021-03-25T03:51:31.476516Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af90065c904d467eaad1d8985660eed0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=469.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "cnn_res_results = train_network(cnn_res_model, loss_func, train_loader, test_loader=test_loader, epochs=10, score_funcs={'Accuracy': accuracy_score}, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:55:51.847484Z",
     "start_time": "2021-03-25T03:55:51.580956Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_results, label='CNN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_relu_results, label='CNN-ReLU')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_bn_results, label='CNN-ReLU-BN')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=cnn_res_results, label='CNN-ReLU-BN-Res')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:55:52.491247Z",
     "start_time": "2021-03-25T03:55:51.848859Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "zip_file_url = \"https://download.pytorch.org/tutorial/data.zip\"\n",
    "\n",
    "import requests, zipfile, io\n",
    "r = requests.get(zip_file_url)\n",
    "z = zipfile.ZipFile(io.BytesIO(r.content))\n",
    "z.extractall()\n",
    "\n",
    "#Zip file is organized as data/names/[LANG].txt , where [LANG] is a specific language\n",
    "\n",
    "namge_language_data = {}\n",
    "\n",
    "#We will use some code to remove UNICODE tokens to make life easy for us processing wise\n",
    "#e.g., convert something like \"Ślusàrski\" to Slusarski\n",
    "import unicodedata\n",
    "import string\n",
    "\n",
    "all_letters = string.ascii_letters + \" .,;'\"\n",
    "n_letters = len(all_letters)\n",
    "alphabet = {}\n",
    "for i in range(n_letters):\n",
    "    alphabet[all_letters[i]] = i\n",
    "    \n",
    "# Turn a Unicode string to plain ASCII, thanks to https://stackoverflow.com/a/518232/2809427\n",
    "def unicodeToAscii(s):\n",
    "    return ''.join(\n",
    "        c for c in unicodedata.normalize('NFD', s)\n",
    "        if unicodedata.category(c) != 'Mn'\n",
    "        and c in all_letters\n",
    "    )\n",
    "\n",
    "\n",
    "#Loop through every language, open the zip file entry, and read all the lines from the text file. \n",
    "for zip_path in z.namelist():\n",
    "    if \"data/names/\" in zip_path and zip_path.endswith(\".txt\"):\n",
    "        lang = zip_path[len(\"data/names/\"):-len(\".txt\")]\n",
    "        with z.open(zip_path) as myfile:\n",
    "            lang_names = [unicodeToAscii(line).lower() for line in str(myfile.read(), encoding='utf-8').strip().split(\"\\n\")]\n",
    "            namge_language_data[lang] = lang_names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:55:52.499914Z",
     "start_time": "2021-03-25T03:55:52.492747Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset = LanguageNameDataset(namge_language_data, alphabet)#Reusing our code from chapter 4\n",
    "\n",
    "train_lang_data, test_lang_data = torch.utils.data.random_split(dataset, (len(dataset)-300, 300))\n",
    "train_lang_loader = DataLoader(train_lang_data, batch_size=32, shuffle=True, collate_fn=pad_and_pack)\n",
    "test_lang_loader = DataLoader(test_lang_data, batch_size=32, shuffle=False, collate_fn=pad_and_pack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:55:52.506213Z",
     "start_time": "2021-03-25T03:55:52.501232Z"
    },
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:56:47.831891Z",
     "start_time": "2021-03-25T03:55:52.507467Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "391a5bde70fc4ad191323a11570f706b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "rnn_3layer = nn.Sequential( #Simple old style RNN \n",
    "  EmbeddingPackable(nn.Embedding(len(all_letters), 64)), #(B, T) -> (B, T, D)\n",
    "  nn.RNN(64, n, num_layers=3, batch_first=True), #(B, T, D) -> ( (B,T,D) , (S, B, D)  )\n",
    "  LastTimeStep(rnn_layers=3), #We need to take the RNN output and reduce it to one item, (B, D)\n",
    "  nn.Linear(n, len(namge_language_data)), #(B, D) -> (B, classes)\n",
    ")\n",
    "\n",
    "#Apply gradient cliping to maximize its performance\n",
    "for p in rnn_3layer.parameters():\n",
    "    p.register_hook(lambda grad: torch.clamp(grad, -5, 5))\n",
    "\n",
    "rnn_results = train_network(rnn_3layer, loss_func, train_lang_loader, test_loader=test_lang_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:57:46.294852Z",
     "start_time": "2021-03-25T03:56:47.833461Z"
    },
    "tags": [
     "remove_output"
    ]
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7051bfad6ff4b7da08f766b5d1486bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Training'), FloatProgress(value=0.0, max=618.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Testing'), FloatProgress(value=0.0, max=10.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "lstm_3layer = nn.Sequential(\n",
    "  EmbeddingPackable(nn.Embedding(len(all_letters), 64)), #(B, T) -> (B, T, D)\n",
    "  #nn.RNN became nn.LSTM, and now we are upgraded to LSTMs w/ peephole connections\n",
    "  nn.LSTM(64, n, num_layers=3, batch_first=True), #(B, T, D) -> ( (B,T,D) , (S, B, D)  )\n",
    "  LastTimeStep(rnn_layers=3), #We need to take the RNN output and reduce it to one item, (B, D)\n",
    "  nn.Linear(n, len(namge_language_data)), #(B, D) -> (B, classes)\n",
    ")\n",
    "#We still want to use gradient clipping with every kind of RNN, including LSTMs\n",
    "for p in lstm_3layer.parameters():\n",
    "    p.register_hook(lambda grad: torch.clamp(grad, -5, 5))\n",
    "\n",
    "lstm_results = train_network(lstm_3layer, loss_func, train_lang_loader, test_loader=test_lang_loader, score_funcs={'Accuracy': accuracy_score}, device=device, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-25T03:57:46.543141Z",
     "start_time": "2021-03-25T03:57:46.296253Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='epoch', ylabel='test Accuracy'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='epoch', y='test Accuracy', data=rnn_results, label='RNN: 3-Layer BiDir')\n",
    "sns.lineplot(x='epoch', y='test Accuracy', data=lstm_results, label='LSTM: 3-Layer BiDir')"
   ]
  }
 ],
 "metadata": {
  "author": "Proof that Batch-Norm does not add representational power",
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "latex_metadata": {
   "title": "Common Design Building Blocks"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
